Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol.

Purpose: To evaluate the performance of artificial intelligence (AI) systems embedded in a mobile, handheld retinal camera, with a single retinal image protocol, in detecting both diabetic retinopathy (DR) and more-than-mild diabetic retinopathy (mtmDR). Design: Multicenter cross-sectional diagnostic study, conducted at 3 diabetes care and eye care facilities. Participants: A total of 327 individuals with diabetes mellitus (type 1 or type 2) underwent a retinal imaging protocol enabling expert reading and automated analysis. Methods: Participants underwent fundus photographs using a portable retinal camera (Phelcom Eyer). The captured images were automatically analyzed by deep learning algorithms retinal alteration score (RAS) and diabetic retinopathy alteration score (DRAS), consisting of convolutional neural networks trained on EyePACS data sets and fine-tuned using data sets of portable device fundus images. The ground truth was the classification of DR corresponding to adjudicated expert reading, performed by 3 certified ophthalmologists. Main Outcome Measures: Primary outcome measures included the sensitivity and specificity of the AI system in detecting DR and/or mtmDR using a single-field, macula-centered fundus photograph for each eye, compared with a rigorous clinical reference standard comprising the reading center grading of 2-field imaging protocol using the International Classification of Diabetic Retinopathy severity scale. Results: Of 327 analyzed patients (mean age, 57.0 ± 16.8 years; mean diabetes duration, 16.3 ± 9.7 years), 307 completed the study protocol. Sensitivity and specificity of the AI system were high in detecting any DR with DRAS (sensitivity, 90.48% [95% confidence interval (CI), 84.99%-94.46%]; specificity, 90.65% [95% CI, 84.54%-94.93%]) and mtmDR with the combination of RAS and DRAS (sensitivity, 90.23% [95% CI, 83.87%-94.69%]; specificity, 85.06% [95% CI, 78.88%-90.00%]). The area under the receiver operating characteristic curve was 0.95 for any DR and 0.89 for mtmDR. Conclusions: This study showed a high accuracy for the detection of DR in different levels of severity with a single retinal photo per eye in an all-in-one solution, composed of a portable retinal camera powered by AI. Such a strategy holds great potential for increasing coverage rates of screening programs, contributing to prevention of avoidable blindness. Financial Disclosures: F.K.M. is a medical consultant for Phelcom Technologies. J.A.S. is Chief Executive Officer and proprietary of Phelcom Technologies. D.L. is Chief Technology Officer and proprietary of Phelcom Technologies. P.V.P. is an employee at Phelcom Technologies.

Turbulence hierarchy in foreign exchange markets.

We present a multiscale stochastic analysis of foreign exchange rates using the H-theory formalism, which provides a hierarchical intermittency model for the information cascade in the currency market. We examine the distributions of returns and volatilities for the three most traded currency pairs: euro-U.S. dollar, U.S. dollar-Japanese yen, and British pound-U.S. dollar. We find that these markets have a hierarchy of timescales, with larger markets exhibiting more hierarchy levels. We provide a theoretical framework for understanding why the number of levels in the information cascade increases with market size, in analogy with similar behavior for the energy cascade in turbulence as a function of Reynolds number. We briefly argue that using turbulence-like models for financial markets can also provide valuable insights for developing efficient algorithmic trading strategies.

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts.

Impact of preaneurysmal M1 length in unruptured middle cerebral artery aneurysm: mid-term outcome and single-center experience.

OBJECTIVE: This study was design to investigate the surgical and functional outcome based on the preaneurysmal M1 length for unruptured MCA aneurysm. METHODS: Among 250 consecutive patients with unruptured aneurysms operated in our institution between 2015 and 2017, 72 were MCA aneurysms. Risk factors for IR (i.e., intraoperative rupture) were investigated including age, sex, preaneurysmal M1 length, maximal MCA aneurysm diameter, neck size, aneurysm shape, sphenoid ridge proximation sign. Outcome was measured at discharge, 1 yr and last follow-up. Outcome was compared according to the preaneurysmal M1 length. RESULTS: Among 68 patients included, five patients (7.3%) suffered IR. Mean maximal diameter of MCA aneurysm (7.9 mm ± 3.4 vs. 4.5 ± 1.8; p = 0.01) was significantly associated with IR risk. Mean M1 length seemed to be shorter in the IR group although not statistically significant (16.2 mm ± 5.1 vs. 11.5 mm ± 4.8; p = 0.053). Mid-term outcome was favorable for all patients at last follow-up but was worsen in case of short preaneurysmal M1 segment (10.7 mm ± 4.8 vs. 16.4 mm ± 5.3, p = 0.02). Complete aneurysm occlusion was achieved for sixty-nine patients (95.5%) with 6.9% of early postoperative complications. CONCLUSIONS: The microsurgical treatment of unruptured MCA aneurysm was associated with favorable mid-term outcome in all patients and high rates of complete occlusion. Aneurysm size was significantly associated with the intraoperative rupture risk for unruptured MCA aneurysm and patients with a short preaneurysmal M1 segment seemed to have a greater risk of intraoperative rupture although not statistically significant. Short preaneurysmal M1 patients had worsen mid-term outcome.

Retrospective case series of high-density silicone oil (Oxane HD) in severe proliferative vitreorretinal retinal detachment patients.

BACKGROUND: Describe complications and clinical outcomes of heavy silicone oil (HSO) Oxane HD® use as an alternative to overcome the challenges of performing vitrectomy to treat tractional and rhegmatogenous retinal detachments with proliferative vitreoretinopathy (PVR). METHODS: A retrospective, observational study was performed on patients from one center from August 2014 to Aug 2023. It was included patients who underwent surgery using HSO Oxane HD® to treat rhegmatogenous retinal detachment with PVR or mixed tractional and rhegmatogenous diabetic retinal detachment. Severely ill patients who could not attend to follow up were excluded. The primary outcome was successful retinal attachment at first postoperative month. A descriptive analysis was performed. RESULTS: Among the 31 patients, 29 (93.5%) underwent surgeries due to rhegmatogenous retinal detachment and two (6.5%) for diabetic retinal detachment. The primary anatomic success was achieved in 27 (87.1%) patients. At the final visit, 17 (56.6%) had vision better than 20/400 (range, 20/30 to light perception). The vision was stable or improved in 22 (76.8%) patients at the end of follow-up. Nineteen (61.3%) patients required hypotensive eye drops after HSO use and twelve (38.7%) still required hypotensive eye drops at the final follow-up; three (9.7%) patients required additional glaucoma surgeries. CONCLUSIONS: HSO is safe and useful for complex retinal detachments cases specially with inferior tears and PVR. Ocular hypertension is frequent and usually clinically controlled with hypotensive eyedrops. Close postoperatively follow-up is advised due to the ocular complications, particularly elevated intraocular pressure and emulsification.

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

Neuroproteomics: Unveiling the Molecular Insights of Psychiatric Disorders with a Focus on Anxiety Disorder and Depression.

Anxiety and depression are two of the most common mental disorders worldwide, with a lifetime prevalence of up to 30%. These disorders are complex and have a variety of overlapping factors, including genetic, environmental, and behavioral factors. Current pharmacological treatments for anxiety and depression are not perfect. Many patients do not respond to treatment, and those who do often experience side effects. Animal models are crucial for understanding the complex pathophysiology of both disorders. These models have been used to identify potential targets for new treatments, and they have also been used to study the effects of environmental factors on these disorders. Recent proteomic methods and technologies are providing new insights into the molecular mechanisms of anxiety disorder and depression. These methods have been used to identify proteins that are altered in these disorders, and they have also been used to study the effects of pharmacological treatments on protein expression. Together, behavioral and proteomic research will help elucidate the factors involved in anxiety disorder and depression. This knowledge will improve preventive strategies and lead to the development of novel treatments.

Bibliometric Analysis of Cannabis sativa Research: Trends, Challenges, and Future Directions.

Introduction: The use of medical Cannabis remains unregulated in several countries due to the scarcity of clinical studies with high scientific evidence that establish safety and efficacy of Cannabis products. This study aimed to comprehensively analyze how knowledge has been created in this field, as well as perform a bibliographic mapping to identify knowledge gaps, and investigate key authors and journals that have significantly contributed to advancing our understanding of Cannabis. Method: The study protocol was registered in the International Prospective Register of Systematic Review (CRD42020223084). A systematic search was conducted in the Cochrane Central Register of Controlled Trials, Lilacs, Google Scholar, PubMed, SciELO, Scopus, and Web of Science databases. The bibliographic analysis and mapping were registered using the VOSviewer, Sci2tool, CiteSpace, and PoP software. Results: The systematic search identified 27,597 records, with 17,020 duplicates, resulting in a total of 10,577 articles included. The authors who published the most were Marilyn Ann Huestis (n=108) and Sagnik Bhattacharyya (n=71), while Elisaldo A. Carlini and Raphael Mechoulam published 8 and 22 articles, respectively. The journals Drug and Alcohol Dependence (n=297), Psychopharmacology (n=159) and Addictive Behaviors (n=150) were the ones that published the most on Cannabis. The journals suggest that the articles are correlated with the adverse and toxicological effects of recreational Cannabis use; however, most articles focus on medical Cannabis. The peak of publications was in 2021 (n=1,481). The countries that published the most were the United States (n=9,735), while Brazil occupied the 11th position (n=422). Most publications were carried out in "Pharmacology and Pharmacy" (11.31%), followed by "Psychiatry" (7.66%) and "Medicine" (5.80%). The areas of "Neurosciences" (1.59%), "Biochemistry," "Genetics," and "Molecular Biology" (0.79%) were little explored. Conclusion: This study captured the characteristics of publications about Cannabis and clinical studies in the scientific literature, yielding >10,000 articles, representing a large literature review, to date. Therefore, the most productive countries included the United States, the United Kingdom, and Canada, while the most productive authors were Marilyn Ann Huestis and Sagnik Bhattacharyya, with a peak of publications in 2021. Finally, the most chosen journals were Drug and Alcohol Dependence and Psychopharmacology.

Impact of low-level laser therapy on the quality of life of patients with xerostomia undergoing head and neck radiotherapy: a systematic review.

PURPOSE: To carry out a systematic review to assess whether low-level laser therapy can improve the quality of life of patients with xerostomia undergoing head and neck radiotherapy. METHODS: A systematic search was performed through Embase, Medline/PubMed, Cochrane, Scopus, Web of Science, nonpeer-reviewed clinicaltrials.gov and LILACS. The strategy included clinical studies were selected that prospectively followed or evaluated the quality of life by directly comparing the use of low-level laser therapy for xerostomia induced by head and neck radiotherapy with alternative therapies without the use of a laser. The risk of bias in the studies was assessed by RoB 2.0 and Robins I. RESULTS: After all application of the predetermined criteria, four studies were included, dated between the years 2014 and 2023. Three studies described as randomized clinical trials were included, one of which was a randomized pilot study and only one was a prospective clinical trial. A total of 126 patients were evaluated, all four studies used the infrared wavelength, with two studies using the combination with the red wavelength. It was observed that low-level laser therapy can change the sensation of dry mouth, improving patients' quality of life. In addition, changes related to increased stimulated and unstimulated salivary flow were also identified. CONCLUSION: The use of low-level laser therapy has promising results on xerostomia, consequently improving the quality of life of patients undergoing radiotherapy in the head and neck region.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Breath-holding as model for the evaluation of EEG signal during respiratory distress.

PURPOSE: Research describes the existence of a relationship between cortical activity and the regulation of bulbar respiratory centers through the evaluation of the electroencephalographic (EEG) signal during respiratory challenges. For example, we found evidences of a reduction in the frequency of the EEG (alpha band) in both divers and non-divers during apnea tests. For instance, this reduction was more prominent in divers due to the greater physiological disturbance resulting from longer apnea time. However, little is known about EEG adaptations during tests of maximal apnea, a test that voluntarily stops breathing and induces dyspnea. RESULTS: Through this mini-review, we verified that a protocol of successive apneas triggers a significant increase in the maximum apnea time and we hypothesized that successive maximal apnea test could be a powerful model for the study of cortical activity during respiratory distress. CONCLUSION: Dyspnea is a multifactorial symptom and we believe that performing a successive maximal apnea protocol is possible to understand some factors that determine the sensation of dyspnea through the EEG signal, especially in people not trained in apnea.

Improving the Quality of Residency Programs in Family and Community Medicine.

OBJECTIVE: To develop and present an instrument to evaluate and monitor the quality of medical residency programs in residencies in family and community medicine (FCM) based on preceptors and residents, considering the insertion of the health network program. METHOD: The instrument was developed in three stages: 1) interview with the preceptors of FCM; 2) literature review; and 3) production, adequacy, and approval of the evaluation instrument by renowned professionals of the Brazilian FCM. The third stage included 9 people and used the Delphi technique with 80% agreement. For the qualitative results, Bardin's Content Analysis was used. RESULTS: In all, there were five evaluation cycles to adapt the proposed recommendations, with the elimination of one item and weighting, with a results analysis methodology of 10 resulting items, reaching an expected matrix for organizing residency programs in the health network, divided into 3 domains: Organization of the Unit, Human Resources, and Preceptor-resident relationship. CONCLUSION: An instrument for evaluating and monitoring residency programs in family and community medicine can be a tool to facilitate program managers and allow evaluation and monitoring, continuously qualifying them.

Recovery course of persistent posterior subretinal fluid after successful repair of rhegmatogenous retinal detachment.

PURPOSE: To investigate best corrected visual acuity (BCVA), subretinal fluid (SRF) absorption time or ellipsoid zone (EZ) restoration time and various variables in patients with persistent SRF after successful primary repair of rhegmatogenous retinal detachment (RRD). METHODS: This retrospective multicenter study allowed independent analysis of the healing pattern by two observers based on composite of serial cross-sectional macular optical coherence tomography (OCT) scans. Univariate and multivariate analyses were implemented. RESULTS: One hundred and three cases had persistent SRF after pars plana vitrectomy, scleral buckling, or pneumatic retinopexy. By univariate analysis, SRF resolution time correlated positively with the number of retinal breaks (p < 0.001) and with increased myopia (p = 0.011). Using multivariate analysis, final BCVA (log MAR) correlated positively with age, duration of RRD, initial BCVA (OR = 3.28; [95%CI = 1.44-7.47]; p = 0.015), and SRF resolution time (OR = 0.46 [95%CI 0.21-1.05]; p = 0.049). EZ restoration time was longer with increasing number of retinal tears (OR = 0.67; [95%CI 0.29-1.52]; p = 0.030), worse final BCVA, and presence of macula-off RRD (OR = 0.26; [95%CI 0.08-0.88]; p = 0.056). SRF resolution time correlated marginally with prone position. CONCLUSIONS: Residual posterior SRF is more common in eyes with multiple breaks or in myopic eyes. Final BCVA is better in younger subjects and in eyes with shorter duration of RRD. Persistent SRF is a self-limited disorder with a mean resolution of 11.2 months with good visual prognosis improving from a mean baseline logMAR of 1.08 to 0.25 at one year.

Artificial intelligence for telemedicine diabetic retinopathy screening: a review.

PURPOSE: This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. METHODS: The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups. RESULTS: The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation. CONCLUSIONS: Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.

Measuring NLR Oligomerization II: Detection of ASC Speck Formation by Confocal Microscopy and Immunofluorescence.

Inflammasomes are crucial sentinels of the innate immune system that sense clues of infection, cellular stress, or metabolic imbalances. Upon activation, the inflammasome sensor (e.g., NLRP3) recruits the adaptor protein apoptosis-associated speck-like protein containing a CARD (ASC). ASC rapidly oligomerizes to form a micron-sized structure termed "ASC speck." These are crucial for the activation of caspase-1 and downstream inflammatory signals released following a specific form of lytic cell death called pyroptosis. Hence, due to their considerably large size, ASC specks can be easily visualized by microscopy as a simple upstream readout for inflammasome activation. Here, we provide three detailed protocols for imaging ASC specks: (1) live-cell imaging of macrophage cell lines expressing a fluorescent protein fusion form of ASC, (2) imaging of human primary cells using immunofluorescence staining of endogenous ASC, and (3) visualization and quantification of specks on a single-cell level using imaging flow cytometry.

Application of two- and multiway chemometric strategies for describing elementomic changes in pepper plants exposed to cadmium stress by multielement determination.

The objective of this work was to evaluate elemental changes in pepper exposed to Cd stress through different chemometric tools. For this purpose, pepper plants were grown under five different treatments with different Cd concentrations in the nutrient solution. Considering the hypothesis that pepper plants exposed to Cd stress during growth undergo changes in the macro- and microelemental distribution in leaves, stems, and roots, principal component analysis (PCA) and parallel factor (PARAFAC) analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes in pepper plants. Since the number of variables and the data generated were large and complex, the application of chemometric tools was justified to facilitate the visualization and interpretation of results. The mineral composition, namely the Ca, Cd, Cu, Fe, K, Mg, Mn, N, and P contents, was assessed in 180 samples of leaves, stems, and roots of the cultivated peppers. Then, PCA and PARAFAC analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes throughout pepper plants. The visualization of the trend on each sample and their intrinsic relationship with the variables were possible with the application of PCA. The use of PARAFAC analysis permitted the simultaneous study of all samples in a straightforward representation of the information that facilitated a quick and comprehensive understanding of the spatial distribution of elements in plants. Thus, macroelements (Ca, K, Mg, N, and P) that were found in higher concentrations in leaves did not present significant differences in the distribution along the plants under different treatment conditions. In contrast, a significant impact on the microelement (Cu, Fe, and Mn) distribution was produced between uncontaminated and contaminated samples. This analysis revealed a significant accumulation of Cd in roots and adverse effects on normal plant growth, demonstrating their level of phytotoxicity to pepper.

Salmonella detection with LAMP and qPCR and identification of serovars of interest by multiplex qPCR in poultry carcasses.

Salmonella is present in the poultry production chain and is a major challenge in terms of food safety and animal health. The early Salmonella detection is one of the main tools to control and prevent the transmission of this pathogen. Microbiological isolation and serotyping to identify and differentiate Salmonella serovars are laborious processes, time-consuming, and expensive. Therefore, molecular diagnostic methods can be rapid and efficient alternatives to the detection of this pathogen. Thus, the aim herein was to standardize and evaluate the use of loop-mediated isothermal amplification (LAMP) in comparison with real-time PCR (qPCR) for detection of Salmonella associated with a multiplex qPCR for simultaneous identification and differentiation of S. Enteritidis, S. Typhimurium, S. Pullorum, and S. Gallinarum. The LAMP, qPCR, and multiplex qPCR assays were comparable in specificity. The three techniques were evaluated for specificity for 16 different serovars of Salmonella and for 37 strains of the serovars of interest. The limit of detection and the efficiency of the LAMP, qPCR, and multiplex qPCR reactions were determined. The techniques were applied to 33 samples of chicken carcasses and compared to the results of conventional microbiology for validation. As results, LAMP was specific in the detection of different Salmonella serovars but presented lower limit of detection ranging from 101 to 104 CFU/reaction. In comparison, qPCR could detect less cells (100 to 102 CFU/reaction), reaching equal specificity and better repeatability in the assays. The qPCR multiplexing for identification of the different serovars also showed good specificity, with the detection threshold between entre 101 and 102 CFU/reaction. The results obtained in the analyses on poultry carcasses suggested a correspondence between the results obtained in molecular methods and in conventional microbiology. Thus, the proposed assays are promising for the diagnosis of Salmonella in poultry carcasses, already proved to be faster and more efficient than conventional diagnostics techniques, being of great interest for poultry production, animal, and public health.

Fairness and generalizability of OCT normative databases: a comparative analysis.

PURPOSE: In supervised Machine Learning algorithms, labels and reports are important in model development. To provide a normality assessment, the OCT has an in-built normative database that provides a color base scale from the measurement database comparison. This article aims to evaluate and compare normative databases of different OCT machines, analyzing patient demographic, contrast inclusion and exclusion criteria, diversity index, and statistical approach to assess their fairness and generalizability. METHODS: Data were retrieved from Cirrus, Avanti, Spectralis, and Triton's FDA-approval and equipment manual. The following variables were compared: number of eyes and patients, inclusion and exclusion criteria, statistical approach, sex, race and ethnicity, age, participant country, and diversity index. RESULTS: Avanti OCT has the largest normative database (640 eyes). In every database, the inclusion and exclusion criteria were similar, including adult patients and excluding pathological eyes. Spectralis has the largest White (79.7%) proportionately representation, Cirrus has the largest Asian (24%), and Triton has the largest Black (22%) patient representation. In all databases, the statistical analysis applied was Regression models. The sex diversity index is similar in all datasets, and comparable to the ten most populous contries. Avanti dataset has the highest diversity index in terms of race, followed by Cirrus, Triton, and Spectralis. CONCLUSION: In all analyzed databases, the data framework is static, with limited upgrade options and lacking normative databases for new modules. As a result, caution in OCT normality interpretation is warranted. To address these limitations, there is a need for more diverse, representative, and open-access datasets that take into account patient demographics, especially considering the development of supervised Machine Learning algorithms in healthcare.

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.

BACKGROUND: Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts. METHODS: Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones. RESULTS: All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models. CONCLUSION: The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.

Implementation of family and community medicine residency in primary care: use of the implementation analysis tool in state of São Paulo / La implementación de la residencia en medicina familiar y comunitaria en la atención primaria: uso de la herramienta de análisis de implementación en el estado de São Paulo. / A implantação da residência de medicina de família e comunidade na atenção primária: uso da ferramenta de análise de implantação no estado de São Paulo

Introduction: Family and community medicine (FCM) is the preferred specialty to be present in the main gateway to health systems, primary health care (PHC). The best way to increase this specialty is the medical residency, in which at least 70% of the internship period is in the PHC. Thus, the quality of implantation of residency in the PHC is essential. Objectives: Evaluate the quality of implementation of medical residency programs in FCM in PHC. Method: Use of a tool developed to analyze the implementation of FCM residency in PHC in small (up to two second-year residents), medium (two to five) and large (from six) programs, evaluating from zero to four points , between not implanted and fully implanted. The grades were obtained from interviews with residents, preceptors, coordinators and municipal managers, considering the fourth generation assessment. Results: Six programs were evaluated, in municipalities with 20,000 to 700,000 inhabitants, ranging from one to 22 second-year residents, ranging from unsatisfactory (one program) to fully implemented (two programs). Municipalities with greater PHC coverage showed greater implementation results. The lowest scores were in the items "permanent education" and "continuing education" and the highest in the presence of FCM specialists as preceptors. There is a difference in perception among respondents considering the same areas. Discussion: The study suggests that municipalities with greater investment in PHC also have better residency programs, regardless of whether the link is with educational centers or health departments. The SARS-CoV-2 pandemic has also hampered health education. The results were also defining when interviewing different people, demonstrating that this methodology is essential. Conclusion: There is a need to observe the implementation of residency programs in PHC to ensure quality training, and not just quantity to provide.

Introducción: La medicina familiar y comunitaria (MFC) es la especialidad preferida para estar presente en la principal puerta de entrada de los sistemas de salud, la atención primaria de salud (APS). La mejor forma de incrementar esta especialidad es la residencia médica, en la que al menos el 70% del período de prácticas es en la APS. Así, la calidad de implantación de la residencia en la APS es fundamental. Objetivos: Evaluar la calidad de la implementación de los programas de residencia médica en FCM en la APS. Método: Uso de una herramienta desarrollada para analizar la implementación de la residencia de FCM en APS en programas pequeños (hasta dos residentes de segundo año), medianos (de dos a cinco) y grandes (de seis), evaluando de cero a cuatro puntos, entre no implantado y totalmente implantado. Las calificaciones se obtuvieron a partir de entrevistas con vecinos, preceptores, coordinadores y administradores municipales, considerando la evaluación de cuarta generación. Resultados: Se evaluaron seis programas, en municipios de 20.000 a 700.000 habitantes, con rango de uno a 22 residentes de segundo año, desde insatisfactorio (un programa) hasta totalmente implementado (dos programas). Los municipios con mayor cobertura de APS mostraron mayores resultados de implementación. Los puntajes más bajos fueron en los ítems "educación permanente" y "educación continua" y los más altos en presencia de especialistas de la FCM como preceptores. Existe una diferencia de percepción entre los encuestados considerando las mismas áreas. Discusión: El estudio sugiere que los municipios con mayor inversión en APS también tienen mejores programas de residencia, independientemente de que el vínculo sea con centros educativos o departamentos de salud. La pandemia del SARS-CoV-2 también ha dificultado la educación sanitaria. Los resultados también fueron definitorios al entrevistar a diferentes personas, demostrando que esta metodología es fundamental. Conclusión: Existe la necesidad de observar la implementación de programas de residencia en APS para garantizar una formación de calidad, y no solo cantidad a impartir.

Introdução: A medicina de família e comunidade (MFC) é a especialidade preferencial para estar presente na principal porta de entrada dos sistemas de saúde, a atenção primária à saúde (APS). A melhor forma de incremento dessa especialidade é a residência médica, em que ao menos 70% do período de estágio é na APS. Assim, é imprescindível a qualidade de implantação da residência na APS. Objetivos: Avaliar a qualidade de implementação dos programas de residência médica em MFC na APS. Método: Utilização de uma ferramenta desenvolvida para análise de implementação de residência de MFC na APS em programas pequenos (até dois residentes do segundo ano), médios (dois a cinco) e grandes (a partir de seis), avaliando de zero a quatro pontos, entre não implantado e totalmente implantado. As notas foram obtidas a partir de entrevista com residentes, preceptores, coordenadores e gestores municipais, considerando avaliação de quarta geração. Resultados: Seis programas foram avaliados, em municípios de 20 mil a 700 mil habitantes, com variação de um a 22 residentes do segundo ano, variando de insatisfatório (um programa) a totalmente implantado (dois programas). Municípios com maior cobertura de APS apresentaram resultados de implementação maiores. As notas mais baixas foram nos itens "educação permanente" e "educação continuada" e as mais altas na presença de especialistas em MFC como preceptores. Há diferença de percepção entre os entrevistados considerando as mesmas áreas. Discussão: O estudo sugere que municípios com maior investimento na APS também possuem melhores programas de residência, independentemente de o vínculo ser com centros educacionais ou secretarias de saúde. A pandemia de SARS-CoV-2 também dificultou a educação em saúde. Os resultados também foram definidores quando entrevistadas diferentes pessoas, demonstrando ser essencial essa metodologia. Conclusão: Há a necessidade de observar a implementação dos programas de residência na APS para garantir formação de qualidade, e não apenas quantidade para provimento.