Pancreatic Pseudocyst and Obesity: Video Case Report of Management with the One-Stage Procedure.

Reports of pancreatic pseudocyst drainage during metabolic bariatric surgery are extremely rare. Our patient is a 38-year-old female suffering from obesity grade IV and presents a persistent symptomatic pancreatic pseudocyst 8 months after an episode of acute biliary pancreatitis. After an extensive evaluation and considering other treatment options, our multidisciplinary team and the patient decided to perform a one-stage procedure consisting of laparoscopic cystogastrostomy, cholecystectomy, and one-anastomosis gastric bypass. After bringing the patient to the operating room, the surgeon performed an anterior gastrostomy to access the stomach's posterior wall, followed by a 6-cm cystogastrostomy on both the stomach's posterior wall and the cyst. Next, a cholecystectomy which involved dissecting the triangle of Calot was performed. Then, an 18-cm gastric pouch using a 36-Fr calibration tube was created. The cystogastrostomy was left in the remaining stomach. Finally, gastrojejunal anastomosis is done. The patient's postoperative course proceeded smoothly, leading to her home discharge on the third postoperative day. At the 1-year follow-up, the patient had lost 56 kg and was symptom-free; a computer tomography scan showed that the pancreatic pseudocyst had resolved. This case shows a video of a successful laparoscopic cystogastrostomy, cholecystectomy, and one-anastomosis gastric bypass (OAGB) used to treat persistent abdominal pain and obesity grade IV. We also conduct a bibliographic review.

Study protocol for a randomized controlled trial: Effect of an everyday cognition training program on cognitive function, emotional state, frailty and functioning in older adults without cognitive impairment.

BACKGROUND: Ageing entails changes in complex cognitive functions that lead to a decrease in autonomy and quality of life. Everyday cognition is the ability to solve cognitively complex problems in the everyday world, enabling instrumental activities of life. Benefits have been found in studies using everyday cognition-based assessment and intervention, as the results predict improvements in everyday performance, not just in specific cognitive functions. A study protocol is presented based on assessment and training in everyday cognition versus traditional cognitive stimulation for the improvement of functionality, emotional state, frailty and cognitive function. METHODS: A parallel randomised controlled clinical trial with two arms will be conducted. It will be carried out by the University of Salamanca (Spain) in eleven centres and associations for the elderly of the City Council of Salamanca. People aged 60 years or older without cognitive impairment will be recruited. Participants will be randomly distributed into two groups: the experimental group will undergo a training programme in everyday cognition and the control group a programme of traditional cognitive stimulation, completing 25 sessions over 7 months. All participants will be assessed at the beginning and at the end of the intervention, where socio-demographic data and the following scales will be collected: The Medical Outcomes Study (MOS), Questionnaire ARMS-e, Everyday Cognition Test (PECC), Scale Yesavage, Test Montreal Cognitive Assessment (MoCA), The Functional Independence Measure (FIM), Fragility Index and Lawton y Brody Scale. DISCUSSION: The present study aims to improve conventional clinical practice on cognitive function training by proposing a specific assessment and intervention of everyday cognition based on the importance of actual cognitive functioning during the resolution of complex tasks of daily life, giving priority to the improvement of autonomy. TRIAL REGISTRATION: ClinicalTrials.gov; ID: NCT05688163. Registered on: January 18, 2023.

Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods.

BACKGROUND: Estimating the risk of a difficult tracheal intubation should help clinicians in better anaesthesia planning, to maximize patient safety. Routine bedside screenings suffer from low sensitivity. OBJECTIVE: To develop and evaluate machine learning (ML) and deep learning (DL) algorithms for the reliable prediction of intubation risk, using information about airway morphology. METHODS: Observational, prospective cohort study enrolling n=623 patients who underwent tracheal intubation: 53/623 difficult cases (prevalence 8.51%). First, we used our previously validated deep convolutional neural network (DCNN) to extract 2D image coordinates for 27 + 13 relevant anatomical landmarks in two preoperative photos (frontal and lateral views). Here we propose a method to determine the 3D pose of the camera with respect to the patient and to obtain the 3D world coordinates of these landmarks. Then we compute a novel set of dM=59 morphological features (distances, areas, angles and ratios), engineered with our anaesthesiologists to characterize each individual's airway anatomy towards prediction. Subsequently, here we propose four ad hoc ML pipelines for difficult intubation prognosis, each with four stages: feature scaling, imputation, resampling for imbalanced learning, and binary classification (Logistic Regression, Support Vector Machines, Random Forests and eXtreme Gradient Boosting). These compound ML pipelines were fed with the dM=59 morphological features, alongside dD=7 demographic variables. Here we trained them with automatic hyperparameter tuning (Bayesian search) and probability calibration (Platt scaling). In addition, we developed an ad hoc multi-input DCNN to estimate the intubation risk directly from each pair of photographs, i.e. without any intermediate morphological description. Performance was evaluated using optimal Bayesian decision theory. It was compared against experts' judgement and against state-of-the-art methods (three clinical formulae, four ML, four DL models). RESULTS: Our four ad hoc ML pipelines with engineered morphological features achieved similar discrimination capabilities: median AUCs between 0.746 and 0.766. They significantly outperformed both expert judgement and all state-of-the-art methods (highest AUC at 0.716). Conversely, our multi-input DCNN yielded low performance due to overfitting. This same behaviour occurred for the state-of-the-art DL algorithms. Overall, the best method was our XGB pipeline, with the fewest false negatives at the optimal Bayesian decision threshold. CONCLUSIONS: We proposed and validated ML models to assist clinicians in anaesthesia planning, providing a reliable calibrated estimate of airway intubation risk, which outperformed expert assessments and state-of-the-art methods. Our novel set of engineered features succeeded in providing informative descriptions for prognosis.

Prediction of mortality in acute pulmonary embolism in cancer-associated thrombosis (MAUPE-C): derivation and validation of a multivariable model.

Optimal risk stratification of patients with cancer and pulmonary embolism (PE) remains unclear. We constructed a clinical prediction rule (CPR) named 'MAUPE-C' to identify patients with low 30 days mortality. The study retrospectively developed and internally validated a CPR for 30 days mortality in a cohort of patients with cancer and PE (both suspected and unsuspected). Candidate variables were chosen based on the EPIPHANY study, which categorized patients into 3 groups based on symptoms, signs, suspicion and patient setting at PE diagnosis. The performance of 'MAUPE-C' was compared to RIETE and sPESI scores. Univariate analysis confirmed that the presence of symptoms, signs, suspicion and inpatient diagnosis were associated with 30 days mortality. Multivariable logistic regression analysis led to the exclusion of symptoms as predictive variable. 'MAUPE-C' was developed by assigning weights to risk factors related to the ß coefficient, yielding a score range of 0 to 4.5. After receiver operating characteristic (ROC) curve analysis, a cutoff point was established at ≤ 1. Prognostic accuracy was good with an area under the curve (AUC) of 0.77 (95% CI 0.71-0.82), outperforming RIETE and sPESI scores in this cohort (AUC of 0.64 [95% CI 0.57-0.71] and 0.57 [95% CI 0.49-0.65], respectively). Forty-five per cent of patients were classified as low risk and experienced a 2.79% 30 days mortality. MAUPE-C has good prognostic accuracy in identifying patients at low risk of 30 days mortality. This CPR could help physicians select patients for early discharge.

Effectiveness of Different Neurocognitive Intervention Approaches on Functionality in Healthy Older Adults: A Systematic Review.

Subtle loss of functionality in healthy older adults is considered one of the most important predictors of cognitive decline. Neurocognitive interventions are increasingly being used, from a preventive maintenance approach to functional capacity. This study evaluates the effectiveness of different neurocognitive approaches on the functionality of healthy older adults. In this systematic review (CRD42023473944), an extensive search was conducted for articles published in the last 10 years (2013-2023) in the following databases: Medline, Scopus, and Web of Science. A total of 809 trials were identified, of which 18 were considered to be eligible for inclusion in the review. The data revealed heterogeneity in sample size, measures of functional assessment, neurocognitive interventions used, number of sessions, session duration, and time. Traditional cognitive stimulation is shown to have no significant functional benefit, while other less commonly used neurocognitive interventions, such as those based on everyday cognition, are associated with more significant benefits. Moreover, it is demonstrated that although the Instrumental Activities of Daily Living scale (IADL) is the most used test in similar studies, it is not sensitive enough to detect changes in functionality in healthy elderly individuals, with other tests such as the Timed Instrumental Activities of Daily Living (TIADL) being more advantageous. Therefore, a new guideline is proposed for its use in clinical practice and research, using homogeneous study protocols and neurocognitive interventions that allow for the transfer and generalization of results in daily life.

Identification and distribution of a fungal endosymbiotic Alternaria species (Alternaria section Undifilum sp.) in Astragalus garbancillo tissues.

Plants belonging to the genera Astragalus, Oxytropis, Ipomoea, Sida, and Swainsona often contain the toxin swainsonine (SW) produced by an associated fungal symbiont. Consumption of SW-containing plants causes a serious neurological disorder in livestock, which can be fatal. In this study, a fungal endophyte, Alternaria section Undifilum, was identified in Astragalus garbancillo seeds, using polymerase chain reaction (PCR) followed by direct sequencing. In seeds, the SW concentrations were about 4 times higher than in other parts of the plant. Furthermore, microscopic examination demonstrated that the fungus mycelium grows inside the petioles and stems, on the outer surface and inside the mesocarp of the fruit, in the mesotesta and endotesta layers of the seed coat, and inside the endosperm of the seeds. Our results support the notion that the SW-producing fungus is vertically transmitted in the host plant A. garbancillo.

Older Adult Patients in the Emergency Department: Which Patients should be Selected for a Different Approach?

BACKGROUND: While multidimensional and interdisciplinary assessment of older adult patients improves their short-term outcomes after evaluation in the emergency department (ED), this assessment is time-consuming and ill-suited for the busy environment. Thus, identifying patients who will benefit from this strategy is challenging. Therefore, this study aimed to identify older adult patients suitable for a different ED approach as well as independent variables associated with poor short-term clinical outcomes. METHODS: We included all patients ≥65 years attending 52 EDs in Spain over 7 days. Sociodemographic, comorbidity, and baseline functional status data were collected. The outcomes were 30-day mortality, re-presentation, hospital readmission, and the composite of all outcomes. RESULTS: During the study among 96,014 patients evaluated in the ED, we included 23,338 patients ≥65 years-mean age, 78.4±8.1 years; 12,626 (54.1%) women. During follow-up, 5,776 patients (24.75%) had poor outcomes after evaluation in the ED: 1,140 (4.88%) died, 4,640 (20.51) returned to the ED, and 1,739 (7.69%) were readmitted 30 days after discharge following the index visit. A model including male sex, age ≥75 years, arrival by ambulance, Charlson Comorbidity Index ≥3, and functional impairment had a C-index of 0.81 (95% confidence interval, 0.80-0.82) for 30-day mortality. CONCLUSION: Male sex, age ≥75 years, arrival by ambulance, functional impairment, or severe comorbidity are features of patients who could benefit from approaches in the ED different from the common triage to improve the poor short-term outcomes of this population.

Nonadverse COVID-19 evolution predictors: the CoNAE scale. / Predictores de evolución no adversa en pacientes con COVID-19: escala CoNAE (COVID-19 non-adverse evolution).

OBJECTIVES: Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments. MATERIAL AND METHODS: Retrospective cohort study of patients who came to one of our area's national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographic information, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors. RESULTS: The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847). CONCLUSION: We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care.

OBJETIVO: Faltan herramientas para identificar a los pacientes con COVID-19 moderado o leve. El objetivo de este estudio fue identificar variables asociadas a la evolución no adversa y diseñar un modelo predictivo de evolución favorable en pacientes atendidos en servicios de urgencias hospitalarios (SUH) por infección por SARS-CoV-2. METODO: Estudio de cohorte retrospectivo de pacientes con infección por SARS-CoV-2 que acudieron a alguno de los SUH de hospitales públicos de una área por una infección por COVID-19 entre el 1 de julio de 2020 y el 31 de julio de 2021. Los datos recogidos para este estudio incluyeron información sociodemográfica, comorbilidades basales y tratamientos, otros datos de antecedentes y registro de los signos vitales a la llegada (triaje) al SUH. Se utilizaron modelos de regresión logística multivariable multinivel para desarrollar los modelos predictivos. RESULTADOS: Las personas que tuvieron resultados no adversos eran más jóvenes, mujeres, habían recibido dos dosis de la vacuna COVID-19 en el momento del estudio, tenían signos vitales (frecuencia cardiaca-presión diastólica/sistólica, temperatura y saturación de oxígeno) dentro de un rango normal al llegar al triaje del SUH, y no tenían ninguna de las siguientes comorbilidades: insuficiencia cardiaca, enfermedad coronaria, hipertensión arterial, diabetes, enfermedad hepática, demencia, antecedentes de tumores malignos o prescripción de corticosteroides orales sistémicos o inmunosupresores como medicación basal. El modelo tenía un área bajo la curva (ABC) de 0,8404 (IC 95%: 0,8342-0,8466). CONCLUSIONES: Se ha desarrollado una escala de predicción de resultados no adversos que pueden ser útil como herramienta de triaje, así como para determinar el alta segura y para adaptar el nivel de atención que el paciente requiere, no sólo en el SUH, sino también a nivel de atención de emergencia primaria o extrahospitalaria.

Predictores de evolución no adversa en pacientes con COVID-19: escala CoNAE (COVID-19 non-adverse evolution) / Nonadverse COVID-19 evolution predictors: the CoNAE scale

Objetivos: Faltan herramientas para identificar a los pacientes con COVID-19 moderado o leve. El objetivo de este estudio fue identificar variables asociadas a la evolución no adversa y diseñar un modelo predictivo de evolución favorable en pacientes atendidos en servicios de urgencias hospitalarios (SUH) por infección por SARS-CoV-2. Métodos: Estudio de cohorte retrospectivo de pacientes con infección por SARS-CoV-2 que acudieron a alguno de los SUH de hospitales públicos de unaa área por una infección por COVID-19 entre el 1 de julio de 2020 y el 31 de julio de 2021. Los datos recogidos para este estudio incluyeron información sociodemográfica, comorbilidades basales y tratamientos, otros datos de antecedentes y registro de los signos vitales a la llegada (triaje) al SUH. Se utilizaron modelos de regresión logística multivariable multinivel para desarrollar los modelos predictivos. Resultados: Las personas que tuvieron resultados no adversos eran más jóvenes, mujeres, habían recibido dos dosis de la vacuna COVID-19 en el momento del estudio, tenían signos vitales (frecuencia cardiaca-presión diastólica/sistólica, temperatura y saturación de oxígeno) dentro de un rango normal al llegar al triaje del SUH, y no tenían ninguna de las siguientes comorbilidades: insuficiencia cardiaca, enfermedad coronaria, hipertensión arterial, diabetes, enfermedad hepática, demencia, antecedentes de tumores malignos o prescripción de corticosteroides orales sistémicos o inmunosupresores como medicación basal. El modelo tenía un área bajo la curva (ABC) de 0,8404 (IC 95%: 0,8342-0,8466). Conclusiones: Se ha desarrollado una escala de predicción de resultados no adversos que pueden ser útil como herramienta de triaje, así como para determinar el alta segura y para adaptar el nivel de atención que el paciente requiere, no sólo en el SUH, sino también a nivel de atención de emergencia primaria o extrahospitalaria. (AU)

Background and objectives: Tools to identify patients with mild to moderate COVID-19 are as yet unavailable. Our aims were to identify factors associated with nonadverse outcomes and develop a scale to predict nonadverse evolution in patients with COVID-19 (the CoNAE scale) in hospital emergency departments. Methods: Retrospective cohort study of patients who came to one of our area’s national health service hospitals for treatment of SARS-CoV-2 infection from July 1, 2020, to July 31, 2021. From case records we collected sociodemographicinformation, underlying comorbidity and ongoing treatments, other relevant medical history details, and vital constants on arrival for triage. Multilevel multivariable logistic regression models were used to identify predictors. Results: The model showed that patients who had nonadverse outcomes were younger, female, and vaccinated against COVID-19 (2 doses at the time of the study). They arrived with normal vital signs (heart rate, diastolic and systolic pressures, temperature, and oxygen saturation) and had none of the following concomitant diseases or factors: heart failure other heart disease, hypertension, diabetes, liver disease, dementia, history of malignant tumors, and they were not being treated with oral or other systemic corticosteroids or immunosuppressant therapy. The area under the receiver operating characteristic curve for the model was 0.840 (95% CI, 0.834-0.847). Conclusions: We developed the CoNAE scale to predict nonadverse outcomes. This scale may be useful in triage for evaluating patients with COVID-19. It may also help predict safe discharge or plan the level of care that patients require not only in a hospital emergency department but also in urgent primary care settings or out-of-hospital emergency care. (AU)

Alternativas de procesos para la búsqueda de casos de tuberculosis bajo condiciones económicas limitadas. Cuba 2022-2025 / Process Alternatives for the Tuberculosis Case Finding under Limited Economic Conditions. Cuba 2022-2025

Ejecutar procesos efectivos de búsqueda de casos de tuberculosis es crucial para acele-rar el paso hacia su eliminación. El empeoramiento de las condiciones económicas mun-diales y nacionales no nos permite aplicar extensivamente las tecnologías rápidas mo-leculares idóneas de diagnóstico. Consideramos sensato entonces aplicar algoritmos alternativos que satisfagan las necesidades nacionales presentes hasta que las condi-ciones permitan la cobertura completa de las tecnologías moleculares recomendadas. Sugerimos introducir la radiografía digital para todos los algoritmos, utilizar mejor la microscopía de fluorescencia LED y la óptica convencional ya probadas. En conclusión, es preciso que este enfoque de trabajo, que procura optimizar la efectividad y eficiencia del programa, se introduzca en la práctica cotidiana hasta que lo idóneo sea permisible

Executing effective tuberculosis case-finding processes is crucial to accelerate the path towards elimination of the disease. The worsening of global and national economic conditions do not allow us to extensively apply rapid molecular diagnostic technolo-gies. We consider it sensible and necessary to apply alternative algorithms that meet the current national needs, until conditions allow full coverage of the recommended molecular technologies. We suggest introducing digital X-rays for all algorithms, bet-ter use of LED fluorescence microscopy and conventional optics already appropriate-ly tested. In conclusion, it is necessary that this approach that seeks to optimize the effectiveness and efficiency of the Cuban program be introduced into daily practice until the ideal is permissible

Development and Preliminary Validity Study of a Modified Version of the Upper Extremity Fugl-Meyer Assessment for Use in Telerehabilitation.

BACKGROUND/PURPOSE: The Upper Extremity Fugl-Meyer Assessment (UEFMA, maximum 66) is widely used in clinics and research studies to examine poststroke upper extremity (UE) impairment. This study aimed to develop and provide pilot data to support the validity of a remote version of the UEFMA to examine UE impairment after stroke through telerehabilitation. METHODS: Team members developed a remote version of the UEFMA for telerehabilitation (tUEFMA, maximum 44) using subscales II to IV and VII of the UEFMA. Twenty-two participants with moderate to severe arm impairment (UEFMA, median = 19) and chronic stroke (>1 year post) were evaluated using the UEFMA (face-to-face) and the tUEFMA (remotely). A prediction equation was used to identify the function to predict the UEFMA based on the tUEFMA. Intraclass correlation (ICC) was used to test the absolute agreement between the subscales included in the UEFMA and the tUEFMA, and between their 2 normalized total scores. RESULTS: A strong and significant agreement was found between the total scores of the UEFMA and the projected value based on the tUEFMA (ICC = 0.79, P < 0.05). The ICC test also reported a good agreement in subscales II to IV and a poor agreement in subscale VII between the UEFMA and the tUEFMA using a real-time video link. DISCUSSION AND CONCLUSIONS: The study findings suggest that the tUEFMA is a promising tool to remotely examine UE impairment in individuals with chronic stroke and moderate to severe arm impairment. Future research should evaluate additional psychometric properties and clinical utility of the tUEFMA across stroke participants with a broad range of arm impairments.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A441 ).

Impact of prior SARS-COV-2 infection and vaccination on COVID-19 hospital admission and mortality amongst nursing home residents.

BACKGROUND: Nursing home residents (NHRs) have experienced disproportionately high risk of severe outcomes due to COVID-19 infection. AIM: We investigated the impact of COVID-19 vaccinations and previous SARS-CoV-2 episodes in preventing hospitalization and mortality in NHRs. METHODS: Retrospective study of a cohort of all NHRs in our area who were alive at the start of the vaccination campaign. The first three doses of SARS-CoV-2 vaccine and prior COVID-19 infections were registered. The main outcomes were hospital admission and mortality during each follow up. Random effects time-varying Cox models adjusted for age, sex, and comorbidities were fitted to estimate hazard ratios (HRs) according to vaccination status. RESULTS: COVID-19 hospitalization and death rates for unvaccinated NHRs were respectively 2.39 and 1.42 per 10,000 person-days, falling after administration of the second dose (0.37 and 0.34) and rising with the third dose (1.08 and 0.8). Rates were much lower amongst people who had previously had COVID-19. Adjusted HRs indicated a significant decrease in hospital admission amongst those with a two- and three-dose status; those who had had a previous COVID-19 infection had even lower hospital admission rates. Death rates decreased as NHRs received two and three doses, and the probability of death was much lower among those who had previously had the infection. CONCLUSIONS: The effectiveness of current vaccines against severe COVID-19 disease in NHRs remains high and SARS-CoV-2 episodes prior to vaccination entail a major reduction in hospitalization and mortality rates. The protection conferred by vaccines appears to decline in the following months. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04463706.

[Influence of the social deprivation index on outcomes during the COVID-19 pandemic]. / Influencia del índice de privación social en resultados durante la pandemia de COVID-19.

OBJECTIVE: To see the relationship between the population deprivation index and the use of the health services, adverse evolution and mortality during the COVID-19 pandemic. METHOD: Retrospective cohort study of patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022. The data collected included sociodemographic data, comorbidities and prescribed baseline treatments, other baseline data and the deprivation index, estimated by census section. Multivariable multilevel logistic regression models were performed for each outcome variable: death, poor outcome (defined as death or intensive care unit), hospital admission, and emergency room visits. RESULTS: The cohort consists of 371,237 people with SARS-CoV-2 infection. In the multivariable models, a higher risk of death or poor evolution or hospital admission or emergency room visit was observed within the quintiles with the greatest deprivation compared to the quintile with the least. For the risk of being hospitalized or going to the emergency room, there were differences between most quintiles. It has also been observed that these differences occurred in the first and third periods of the pandemic for mortality and poor outcome, and in all due for the risk of being admitted or going to the emergency room. CONCLUSIONS: The groups with the highest level of deprivation have had worse outcomes compared to the groups with lower deprivation rates. It is necessary to carry out interventions that minimize these inequalities.

First report of Mal de Rio Cuarto Virus and a picorna-like virus naturally infecting rice (Oryza sativa) in Argentina.

The first rice virus detected in Argentina was Rice stripe necrosis virus (RSNV), a benyvirus known to cause "entorchamiento" due to its characteristic symptom of leaf crinkling. As part of this study, it was proposed to sequence plants naturally infected with RSNV that presented another symptom such as thickening of veins, serrated edges, chlorosis that turns necrotic and dwarfism to detect the presence of other viruses in mixed infections. We worked with 20 rice plants sampled in the San Javier area (Santa Fe, Argentina) and that were positive for RSNV by serology using anti-RSNV antiserum. Total RNA of 5mg leaf tissue from each plant was extracted separately using a Qiagen RNeasy Plant RNA kit. Ten µg of pooled sample was sent for library preparation using Ribo-Zero Plant Kit + TruSeq RNA Library Prep Kit v2 and sequenced on an Illumina HiSeq 1500, 150 nucleotide (nt) flowcell at the IABIMO-CONICET/INTA (Argentina). The 177,005,442 reads generated were mapped to the Oryza sativa genome (RefSeq GCF_001433935) using Geneious software v.9.1.8 (Biomatters Limited, Auckland, New Zealand) to remove rice reads. The remaining reads (63,756,284) were assembled de novo using rnaviralSPAdes, Galaxy tools (https://usegalaxy.org.au/). Contigs were annotated using the BEST HIT of BLASTN vs. nt and BLASTX vs. the non-redundant sequence database. Forty virus sequences were analyzed using the ORF finder and BLAST tools at NCBI (http://www.ncbi.nlm.nih.gov/). The nt identity was calculated using the SDT 1.2 program (Muhire et al., 2014). The BLASTN results showed the presence of 38 contigs (636 reads) with high nt identity (higher than 97.6%) with Mal de Rio Cuarto virus (MRCV), with 58% genome coverage. Two other contigs (120 reads) had high nt identity to Fuyang picorna-like virus 2 (FpiV2, GenBank access MT317172), with 38% genome coverage. MRCV is a species of the Fijivirus genus, Reoviridae family, with a linear dsRNA genome composed of 10 segments encoding 12 proteins (Matthijnssens et al., 2022). In this work, it was possible to partially sequence the 10 segments of MRCV. Contigs with lengths greater than 1,000nt were detected that correspond to segments S1 (2029nt), S2 (2308nt), S3 (1249nt) and S4 (1067nt) and showed 98.32%, 98.48%, 97.68% and 97.75% nt identity with the reference sequences (GenBank access NC_008733, NC_008730, NC_008732 and NC_008729), respectively. A contig of 400 nt was identified as a capsid protein (CP) gene fragment (S10) with 98.75% nt identity to the reference sequence (NC_008734). The presence of MRCV was confirmed in 3 of the 20 samples by DAS-ELISA serological test using anti-MRCV antiserum. FpiV2 was reported for the first time infecting rice in China and, due to its genomic structure, was proposed as a new member of the Picornaviridae family, but without an assigned genus (Chao et al., 2021). It is a monopartite virus, with a linear ssRNA(+) genome of 9.2kb. Analysis of two sequence fragments (1587nt and 2086nt) revealed that they corresponded to the putative RdRp with 83.9% nt identity (90.2% aa) and the putative CP sequence with 86.7% nt identity (96.3% aa) with the GenBank sequence MT317172, respectively. Detection of this picorna-like virus was further confirmed in 2 of the 20 samples by RT-PCR and Sanger sequencing with virus-specific primers (PL2Fw: 5' TTATTTGTGAGTAACAGCCCAGCAC 3'; PL2Rv: 5' AGACCGAGGACTATGGAAGCCTTTC 3', 540nt). To our knowledge, this is the first report of rice as a natural host of MRCV and may be the second detection of FpiV2 worldwide.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks.

BACKGROUND: A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology. OBJECTIVE: To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology. METHODS: We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models. RESULTS: With annotators' consensus as the 'gold standard', our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view. CONCLUSIONS: We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant.

OBJECTIVE: We identify factors related to SARS-CoV-2 infection linked to hospitalization, ICU admission, and mortality and develop clinical prediction rules. METHODS: Retrospective cohort study of 380,081 patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022, including a subsample of 46,402 patients who attended Emergency Departments (EDs) having data on vital signs. For derivation and external validation of the prediction rule, two different periods were considered: before and after emergence of the Omicron variant, respectively. Data collected included sociodemographic data, COVID-19 vaccination status, baseline comorbidities and treatments, other background data and vital signs at triage at EDs. The predictive models for the EDs and the whole samples were developed using multivariate logistic regression models using Lasso penalization. RESULTS: In the multivariable models, common predictive factors of death among EDs patients were greater age; being male; having no vaccination, dementia; heart failure; liver and kidney disease; hemiplegia or paraplegia; coagulopathy; interstitial pulmonary disease; malignant tumors; use chronic systemic use of steroids, higher temperature, low O2 saturation and altered blood pressure-heart rate. The predictors of an adverse evolution were the same, with the exception of liver disease and the inclusion of cystic fibrosis. Similar predictors were found to be related to hospital admission, including liver disease, arterial hypertension, and basal prescription of immunosuppressants. Similarly, models for the whole sample, without vital signs, are presented. CONCLUSIONS: We propose risk scales, based on basic information, easily-calculable, high-predictive that also function with the current Omicron variant and may help manage such patients in primary, emergency, and hospital care.

Factors related to early readmissions after acute heart failure: a nested case-control study.

AIMS: To describe the main characteristics of patients who were readmitted to hospital within 1 month after an index episode for acute decompensated heart failure (ADHF). METHODS AND RESULTS: This is a nested case-control study in the ReIC cohort, cases being consecutive patients readmitted after hospitalization for an episode of ADHF and matched controls selected from those who were not readmitted. We collected clinical data and also patient-reported outcome measures, including dyspnea, Minnesota Living with Heart Failure Questionnaire (MLHFQ), Tilburg Frailty Indicator (TFI) and Hospital Anxiety and Depression Scale scores, as well as symptoms during a transition period of 1 month after discharge. We created a multivariable conditional logistic regression model. Despite cases consulted more than controls, there were no statistically significant differences in changes in treatment during this first month. Patients with chronic decompensated heart failure were 2.25 [1.25, 4.05] more likely to be readmitted than de novo patients. Previous diagnosis of arrhythmia and time since diagnosis ≥ 3 years, worsening in dyspnea, and changes in MLWHF and TFI scores were significant in the final model. CONCLUSION: We present a model with explanatory variables for readmission in the short term for ADHF. Our study shows that in addition to variables classically related to readmission, there are others related to the presence of residual congestion, quality of life and frailty that are determining factors for readmission for heart failure in the first month after discharge. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03300791. First registration: 03/10/2017.

Reference values of EORTC QLQ-C30, EORTC QLQ-BR23, and EQ-5D-5L for women with non-metastatic breast cancer at diagnosis and 2 years after.

PURPOSE: To obtain reference norms of EORTC QLQ-C30, EORTC QLQ-BR23, and EQ-5D-5L, based on a population of Spanish non-metastatic breast cancer patients at diagnosis and 2 years after, according to relevant demographic and clinical characteristics. METHODS: Multicentric prospective cohort study including consecutive women aged ≥ 18 years with a diagnosis of incident non-metastatic breast cancer from April 2013 to May 2015. Health-related quality of life (HRQoL) questionnaires were administered between diagnosis and beginning the therapy, and 2 years after. HRQoL differences according to age, comorbidity and stage were tested with ANOVA or Chi Square test and multivariate linear regression models. RESULTS: 1276 patients were included, with a mean age of 58 years. Multivariate models of EORTC QLQ-C30 summary score and EQ-5D-5L index at diagnosis and at 2-year follow-up show the independent association of comorbidity and tumor stage with HRQoL. The standardized multivariate regression coefficient of EORTC QLQ-C30 summary score was lower (poorer HRQoL) for women with stage II and III than for those with stage 0 at diagnosis (- 0.11 and - 0.07, p < 0.05) and follow-up (- 0.15 and - 0.10, p < 0.01). The EQ-5D-5L index indicated poorer HRQoL for women with Charlson comorbidity index ≥ 2 than comorbidity 0 both at diagnosis (- 0.13, p < 0.001) and follow-up (- 0.18, p < 0.001). Therefore, we provided the reference norms at diagnosis and at the 2-year follow-up, stratified by age, comorbidity index, and tumor stage. CONCLUSION: These HRQoL reference norms can be useful to interpret the scores of women with non-metastatic breast cancer, comparing them with country-specific reference values for this population.