Reproductive morphophysiology of an iteroparous batch spawner fish: from early follicle development to ovulation.

Batch spawner fishes develop successive clutches of oocytes which allows them to participate in many reproductive cycles during their adult life (iteroparous) and spawn in multiple events within each breeding cycle. Here, ovarian follicular development was morpho-functionally analyzed in females of the iteroparous batch spawner fish Gymnocorymbus ternetzi. To obtain better insights into the reproductive morpho-physiology in batch spawners, the objective of this research was to analyze the dynamics of the follicular development, with its hormonal regulation between two active reproduction events. We found that over 16 days, follicles progressed asynchronously to chromatin nucleolus, Primary and Secondary growth stages of oogenesis with progressive secretion of 17ß-estradiol (E2). During the end of secondary growth, the increase in 17α,20ß-dihydroxy-4-pregnen-3-one (17,20ß-p) was measured relative to the maturation process of the ovarian follicles (e.g., nuclear migration and its rupture during the resumption of meiosis). Interestingly, an additional increase in E2 was observed after fish reproduction, probably related to the recruitment of new batch follicles for secondary growth. We also measured the high values of multiple condition factor post-reproduction measurements, reflecting more energy invested during the pre-reproductive process. We also quantified high concentrations of 17,20ß-p, probably related to the recruitment of a new batch of oogonia to meiosis, presumably secreted by post-ovulatory follicles, after fish reproduction. We finally found that fish without exposure to reproductive stimulus developed a regression phase at day 24, characterized by massive follicle atresia, that allow them to recycle energy and constitutive materials of the follicles invested during oogenesis for another reproductive cycle.

Clinical characteristics and impact on patient-reported outcomes and quality of life of people with ambulatory secondary progressive multiple sclerosis: Discover study.

BACKGROUND: People with secondary progressive multiple sclerosis (pwSPMS) experience increasing disability, which impacts negatively on their health-related quality of life (HRQoL). Our aims were to assess the impact of secondary progressive multiple sclerosis (SPMS) on functional status and HRQoL and describe the clinical profile in this population. METHODS: DISCOVER is an observational, cross-sectional, multicenter study with retrospective data collection in real-world clinical practice in Spain. Sociodemographic and clinical variables, functional and cognitive scales, patient-reported outcomes (PROs), and direct healthcare, and non-healthcare and indirect costs were collected. RESULTS: A total of 297 evaluable pwSPMS with a EDSS score between 3-6.5 participated: 62.3 % were female and 18.9 % had active SPMS. At the study visit, 77 % of them presented an Expanded Disability Scale Score (EDSS) of 6-6.5. Nearly 40 % did not receive any disease-modifying treatment. Regarding the working situation, 61.6 % were inactive due to disability. PROs: 99.3 % showed mobility impairment in EuroQoL-5 Dimensions-5 Levels, and about 60 % reported physical impact on the Multiple Sclerosis Impact Scale-29. Fatigue was present in 76.1 %, and almost 40 % reported anxiety or depression. The Symbol Digit Modalities Test was used to assess cognitive impairment; 80 % of the patients were below the mean score. Participants who presented relapses two years before and had high EDSS scores had a more negative impact on HRQoL. PwSPMS with a negative impact on HRQoL presented a higher cost burden, primarily due to indirect costs. CONCLUSIONS: PwSPMS experience a negative impact on their HRQoL, with a high physical impact, fatigue, cognitive impairment, and a high burden of indirect costs.

Decisional Conflict Regarding Disease-Modifying Treatment Choices Among Patients with Mid-Stage Relapsing-Remitting Multiple Sclerosis.

Purpose: Shared decision-making is critical in multiple sclerosis (MS) due to the uncertainty of the disease trajectory over time and the large number of treatment options with differing efficacy, safety and administration characteristics. The aim of this study was to assess patients' decisional conflict regarding the choice of a disease-modifying therapy and its associated factors in patients with mid-stage relapsing-remitting multiple sclerosis (RRMS). Methods: A multicenter, non-interventional study was conducted. Adult patients with a diagnosis of RRMS (2017 revised McDonald criteria) and disease duration of 3 to 8 years were included. The level of uncertainty experienced by a patient when faced with making a treatment choice was assessed using the 4-item Decisional Conflict Scale. A battery of patient-reported and clinician-rated measures was administered to obtain information on symptom severity, illness perception, illness-related uncertainty, regret, MS knowledge, risk taking behavior, preferred role in the decision-making process, cognition, and self-management. Patients were recruited during routine follow-up visits and completed all questionnaires online using electronic tablets at the hospital. A multivariate logistic regression analysis was conducted. Results: A total of 201 patients were studied. Mean age (Standard deviation) was 38.7 (8.4) years and 74.1% were female. Median disease duration (Interquartile range) was 6.0 (4.0-7.0) years. Median EDSS score was 1.0 (0-2.0). Sixty-seven (33.3%) patients reported a decisional conflict. These patients had lower MS knowledge and more illness uncertainty, anxiety, depressive symptoms, fatigue, subjective symptom severity, a threatening illness perception, and poorer quality of life than their counterparts. Lack of decisional conflict was associated with MS knowledge (Odds ratio [OR]=1.195, 95% CI 1.045, 1.383, p=0.013), self-management (OR=1.049, 95% CI 1.013, 1.093, p=0.018), and regret after a healthcare decision (OR=0.860, 95% CI 0.756, 0.973, p=0.018) in the multivariate analysis. Conclusion: Decisional conflict regarding the selection of a disease-modifying therapy was a common phenomenon in patients with mid-stage RRMS. Identifying factors associated with decisional conflict may be useful to implement preventive strategies that help patients better understand their condition and strengthen their self-management resources.

National expert consensus on home-administered oncologic therapies in Spain.

The diagnosis and treatment of cancer impose a significant emotional and psychological burden on patients, families, and caregivers. Patients undergo several interventions in a hospital setting, and the increasing number of patients requiring extended care and follow-up is driving the demand for additional clinical resources to address their needs. Hospital at Home (HaH) teams have introduced home-administered oncologic therapies that represent a new model of patient-centered cancer care. This approach can be integrated with traditional models and offers benefits to both patients and healthcare professionals (HCPs). Home-administered treatment programs have been successfully piloted globally, demonstrated as a preferred option for most patients and a safe alternative that could reduce costs and hospital burden. The document aims to establish the minimum recommendations for the home administration of oncologic therapies (ODAH) based on a national expert agreement. The expert panel comprised seven leading members from diverse Spanish societies and three working areas: clinical and healthcare issues, logistical and administrative issues, and economic, social, and legal issues. The recommendations outlined in this article were obtained after a comprehensive literature review and thorough discussions. This document may serve as a basis for the future development of home-administered oncologic therapy programs in Spain. .

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.

BACKGROUND: Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS: In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS: Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS: The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection.

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients' lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems.

Electron Spin-Dependent Electrocatalysis for the Oxygen Reduction Reaction in a Chiro-Self-Assembled Iron Phthalocyanine Device.

The chiral-induced spin selectivity effect (CISS) is a breakthrough phenomenon that has revolutionized the field of electrocatalysis. We report the first study on the electron spin-dependent electrocatalysis for the oxygen reduction reaction, ORR, using iron phthalocyanine, FePc, a well-known molecular catalyst for this reaction. The FePc complex belongs to the non-precious catalysts group, whose active site, FeN4, emulates catalytic centers of biocatalysts such as Cytochrome c. This study presents an experimental platform involving FePc self-assembled to a gold electrode surface using chiral peptides (L and D enantiomers), i.e., chiro-self-assembled FePc systems (CSAFePc). The chiral peptides behave as spin filters axial ligands of the FePc. One of the main findings is that the peptides' handedness and length in CSAFePc can optimize the kinetics and thermodynamic factors governing ORR. Moreover, the D-enantiomer promotes the highest electrocatalytic activity of FePc for ORR, shifting the onset potential up to 1.01 V vs. RHE in an alkaline medium, a potential close to the reversible potential of the O2 /H2 O couple. Therefore, this work has exciting implications for developing highly efficient and bioinspired catalysts, considering that, in biological organisms, biocatalysts that promote O2 reduction to water comprise L-enantiomers.

Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.

The impact of SARS-CoV-2 infection remains substantial on a global scale, despite widespread vaccination efforts, early therapeutic interventions, and an enhanced understanding of the disease's underlying mechanisms. At the same time, a significant number of patients continue to develop severe COVID-19, necessitating admission to intensive care units (ICUs). This study aimed to provide evidence concerning the most influential predictors of mortality among critically ill patients with severe COVID-19, employing machine learning (ML) techniques. To accomplish this, we conducted a retrospective multicenter investigation involving 684 patients with severe COVID-19, spanning from 1 June 2020 to 31 March 2023, wherein we scrutinized sociodemographic, clinical, and analytical data. These data were extracted from electronic health records. Out of the six supervised ML methods scrutinized, the extreme gradient boosting (XGB) method exhibited the highest balanced accuracy at 96.61%. The variables that exerted the greatest influence on mortality prediction encompassed ferritin, fibrinogen, D-dimer, platelet count, C-reactive protein (CRP), prothrombin time (PT), invasive mechanical ventilation (IMV), PaFi (PaO2/FiO2), lactate dehydrogenase (LDH), lymphocyte levels, activated partial thromboplastin time (aPTT), body mass index (BMI), creatinine, and age. These findings underscore XGB as a robust candidate for accurately classifying patients with COVID-19.

Personalized Risk Assessment of Hepatic Fibrosis after Cholecystectomy in Metabolic-Associated Steatotic Liver Disease: A Machine Learning Approach.

Metabolic Associated Fatty Liver Disease (MASLD) is a condition that is often present in patients with a history of cholecystectomy. This is because both situations share interconnected metabolic pathways. This study aimed to establish a predictive model that allows for the identification of patients at risk of developing hepatic fibrosis following this surgery, with potential implications for surgical decision-making. A retrospective cross-sectional analysis was conducted in four hospitals using a database of 211 patients with MASLD who underwent cholecystectomy. MASLD diagnosis was established through liver biopsy or FibroScan, and non-invasive test scores were included for analysis. Various Machine Learning (ML) methods were employed, with the Adaptive Boosting (Adaboost) system selected to build the predictive model. Platelet level emerged as the most crucial variable in the predictive model, followed by dyslipidemia and type-2 diabetes mellitus. FIB-4 score proved to be the most reliable non-invasive test. The Adaboost algorithm improved the results compared to the other methods, excelling in both accuracy and area under the curve (AUC). Moreover, this system holds promise for implementation in hospitals as a valuable diagnostic support tool. In conclusion, platelet level (<150,000/dL), dyslipidemia, and type-2 diabetes mellitus were identified as primary risk factors for liver fibrosis in MASLD patients following cholecystectomy. FIB-4 score is recommended for decision-making, particularly when the indication for surgery is uncertain. This predictive model offers valuable insights into risk stratification and personalized patient management in post-cholecystectomy MASLD cases.

A Machine Learning-Based Method for Detecting Liver Fibrosis.

Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.

Prevalence of Antimicrobial Resistance in Gram-Negative Bacteria Bloodstream Infections in Peru and Associated Outcomes: VIRAPERU Study.

Surveillance of antimicrobial resistance among gram-negative bacteria (GNB) is of critical importance, but data for Peru are not available. To fill this gap, a non-interventional hospital-based surveillance study was conducted in 15 hospitals across Peru from July 2017 to October 2019. Consecutive unique blood culture isolates of key GNB (Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter spp.) recovered from hospitalized patients were collected for centralized antimicrobial susceptibility testing, along with linked epidemiological and clinical data. A total of 449 isolates were included in the analysis. Resistance to third-generation cephalosporins (3GCs) was present in 266 (59.2%) GNB isolates. Among E. coli (n = 199), 68.3% showed 3GC resistance (i.e., above the median ratio for low- and middle-income countries in 2020 for this sustainable development goal indicator). Carbapenem resistance was present in 74 (16.5%) GNB isolates, with wide variation among species (0% in E. coli, 11.0% in K. pneumoniae, 37.0% in P. aeruginosa, and 60.8% in Acinetobacter spp. isolates). Co-resistance to carbapenems and colistin was found in seven (1.6%) GNB isolates. Empiric treatment covered the causative GNB in 63.3% of 215 cases. The in-hospital case fatality ratio was 33.3% (92/276). Pseudomonas aeruginosa species and carbapenem resistance were associated with higher risk of in-hospital death. In conclusion, an important proportion of bloodstream infections in Peru are caused by highly resistant GNB and are associated with high in-hospital mortality.

A Machine Learning Method to Identify the Risk Factors for Liver Fibrosis Progression in Nonalcoholic Steatohepatitis.

AIM: Nonalcoholic fatty liver disease (NAFLD) is a silent epidemy that has become the most common chronic liver disease worldwide. Nonalcoholic steatohepatitis (NASH) is an advanced stage of NAFLD, which is linked to a high risk of cirrhosis and hepatocellular carcinoma. The aim of this study is to develop a predictive model to identify the main risk factors associated with the progression of hepatic fibrosis in patients with NASH. METHODS: A database from a multicenter retrospective cross-sectional study was analyzed. A total of 215 patients with NASH biopsy-proven diagnosed were collected. NAFLD Activity Score and Kleiner scoring system were used to diagnose and staging these patients. Noninvasive tests (NITs) scores were added to identify which one were more reliable for follow-up and to avoid biopsy. For analysis, different Machine Learning methods were implemented, being the eXtreme Gradient Booster (XGB) system the proposed algorithm to develop the predictive model. RESULTS: The most important variable in this predictive model was High-density lipoprotein (HDL) cholesterol, followed by systemic arterial hypertension and triglycerides (TG). NAFLD Fibrosis Score (NFS) was the most reliable NIT. As for the proposed method, XGB obtained higher results than the second method, K-Nearest Neighbors, in terms of accuracy (95.05 vs. 90.42) and Area Under the Curve (0.95 vs. 0.91). CONCLUSIONS: HDL cholesterol, systemic arterial hypertension, and TG were the most important risk factors for liver fibrosis progression in NASH patients. NFS is recommended for monitoring and decision making.

Method for Classifying Schizophrenia Patients Based on Machine Learning.

Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.

Hopelessness in Patients with Early-Stage Relapsing-Remitting Multiple Sclerosis.

Background: Hopelessness is a risk factor for depression and suicide. There is little information on this phenomenon among patients with relapsing-remitting multiple sclerosis (RRMS), one of the most common causes of disability and loss of autonomy in young adults. The aim of this study was to assess state hopelessness and its associated factors in early-stage RRMS. Methods: A multicenter, non-interventional study was conducted. Adult patients with a diagnosis of RRMS, a disease duration ≤ 3 years, and an Expanded Disability Status Scale (EDSS) score of 0-5.5 were included. The State-Trait Hopelessness Scale (STHS) was used to measure patients´ hopelessness. A battery of patient-reported and clinician-rated measurements was used to assess clinical status. A multivariate logistic regression analysis was conducted to determine the association between patients' characteristics and state hopelessness. Results: A total of 189 patients were included. Mean age (standard deviation-SD) was 36.1 (9.4) years and 71.4% were female. Median disease duration (interquartile range-IQR) was 1.4 (0.7, 2.1) years. Symptom severity and disability were low with a median EDSS (IQR) score of 1.0 (0, 2.0). A proportion of 65.6% (n=124) of patients reported moderate-to-severe hopelessness. Hopelessness was associated with older age (p=0.035), depressive symptoms (p=<0.001), a threatening illness perception (p=0.001), and psychological and cognitive barriers to workplace performance (p=0.029) in the multivariate analysis after adjustment for confounders. Conclusion: Hopelessness was a common phenomenon in early-stage RRMS, even in a population with low physical disability. Identifying factors associated with hopelessness may be critical for implementing preventive strategies helping patients to adapt to the new situation and cope with the disease in the long term.

Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.

Musa spp. cultivars as a neutralising source against some toxic activities of Bothrops and Crotalus genus snake venoms.

Accidents involving snakes from Bothrops spp. and Crotalus spp. constitute the most important cause of envenomation in Brazil and Argentina. Musa spp. (banana) have been reported to be used in popular medicine against snakebite by the members of the Canudos Settlement, located in Goiás. In this way, the aim of this work was to evaluate the antivenom effect of the Ouro (AA), Prata (AAB), Prata-anã (AAB) and Figo (ABB) cultivars against in vitro (phospholipase, coagulation and proteolytic) and in vivo (lethality and toxicity) activities caused by the venoms and toxicity (Artemia salina nauplii and Danio rerio embryos) of Musa spp. as well as the annotation of chemical compounds possibly related to these activities. From the in vitro antiophidic tests with the sap, we observed 100% inhibition of the phospholipase and coagulant activities with the cultivars Prata-anã and Figo against the venoms of B. alternatus and C. d. collineatus, B. diporus and B. pauloensis, respectively, and neutralisation of the lethality against the B. diporus venom. It was observed that the cultivars of Musa spp. did not show toxicity against Artemia salina nauplii and Danio rerio embryos. The sap analysis via HPLC-MS/MS allowed the annotation of the 13 compounds: abscisic acid, shikimic acid, citric acid, quinic acid, afzelechin, Glp-hexose, glucose, sucrose, isorhamnetin-3-O-galactoside-6-raminoside, kaempferol-3-glucoside-3-raminoside, myricetin-3-O-rutinoside, procyanidin B1 and rutin. Therefore, it can be seen that Musa spp. is a potential therapeutic agent that can act to neutralise the effects caused by snakebites.

Genetic analysis of global faba bean diversity, agronomic traits and selection signatures.

KEY MESSAGE: We identified marker-trait associations for key faba bean agronomic traits and genomic signatures of selection within a global germplasm collection. Faba bean (Vicia faba L.) is a high-protein grain legume crop with great potential for sustainable protein production. However, little is known about the genetics underlying trait diversity. In this study, we used 21,345 high-quality SNP markers to genetically characterize 2678 faba bean genotypes. We performed genome-wide association studies of key agronomic traits using a seven-parent-MAGIC population and detected 238 significant marker-trait associations linked to 12 traits of agronomic importance. Sixty-five of these were stable across multiple environments. Using a non-redundant diversity panel of 685 accessions from 52 countries, we identified three subpopulations differentiated by geographical origin and 33 genomic regions subjected to strong diversifying selection between subpopulations. We found that SNP markers associated with the differentiation of northern and southern accessions explained a significant proportion of agronomic trait variance in the seven-parent-MAGIC population, suggesting that some of these traits were targets of selection during breeding. Our findings point to genomic regions associated with important agronomic traits and selection, facilitating faba bean genomics-based breeding.

The anti-snake activity of Nectandra angustifolia flavonoids on phospholipase A2: In vitro and in silico evaluation.

ETHNOPHARMACOLOGICAL RELEVANCE: Lauraceae family includes Nectandra angustifolia a species widely used in the folk medicine of South America against various maladies. It is commonly used to treat different types of processes like inflammation, pain, and snakebites. Snakes of the Bothrops genus are responsible for about 97% of the ophidic accidents in northeastern Argentina. AIM OF THE STUDY: To evaluate the anti-snake activity of the phytochemicals present in N. angustifolia extracts, identify the compounds, and evaluate their inhibitory effect on phospholipase A2 (PLA2) with in vitro and in silico assays. METHODS: Seasonal variations in the alexiteric potential of aqueous, ethanolic and hexanic extracts were evaluated by inhibition of coagulant, haemolytic, and cytotoxic effects of B. diporus venom. The chemical identity of an enriched fraction obtained by bio-guided fractioning was established by UPLC-MS/MS analysis. Molecular docking studies were carried out to investigate the binding mechanisms of the identified compounds to PLA2 enzyme from snake venom. RESULTS: All the extracts inhibited venom coagulant activity. However, spring ethanolic extract achieved 100% inhibition of haemolytic activity. Bio-guide fractioning led to an enriched fraction (F4) with the highest haemolytic inhibition. Five flavonoids were identified in this fraction; molecular docking and Molecular Dynamics (MD) simulations indicated the binding mechanisms of the identified compounds. The carbohydrates present in some of the compounds had a critical effect on the interaction with PLA2. CONCLUSION: This study shows, for the first time, which compounds are responsible for the anti-snake activity in Nectandra angustifolia based on in vitro and in silico assays. The results obtained in this work support the traditional use of this species as anti-snake in folk medicine.

Oral manifestations associated with inflammatory bowel disease and early endoscopic findings in patients with spondyloarthritis.

BACKGROUND AND AIMS: Spondyloarthritis (SpA) is a group of autoinflammatory disorders, of which the primary extra-articular manifestation is inflammatory bowel disease (IBD). The oral cavity being a part of gastrointestinal tract, is significantly compromised in IBD, and in many cases, it is the first site of clinical manifestations of IBD. This study aimed to identify changes in the oral mucosa associated with the onset of IBD and their association with endoscopic/histological findings. MATERIALS AND METHODS: The study assessed 80 patients with SpA and 52 healthy controls. Oral, rheumatological, and gastroenterological assessments were performed. The ileocolonoscopy was performed via digital magnification chromoendoscopy. The statistical analysis consisted of Chi-square, Fisher's exact, and multiple correspondence discriminant analysis tests. RESULTS: From the disease cohort, 63.0% patients showed oral lesions (p = 0.050). These manifestations ranged from gingivitis (55.0%, p = 0.001), aphthous stomatitis (3.8%, p = 0.091), angular cheilitis (2.6%, p = 0.200), and perioral erythema with scaling (1.3%, p = 0.300). All patients who presented with alterations in colonic mucosa also had oral lesions associated with IBD (p = 0.039), specifically gingivitis/aphthous stomatitis (p = 0.029). CONCLUSION: The patients with SpA without IBD present significant oral signs and symptoms. Gingivitis seems to be the most relevant because of its associations with early endoscopic and histological findings. CLINICAL RELEVANCE: An integral approach to the diagnostic tests that includes evaluations of oral, rheumatological and gastroenterological tissues may favor timely attention and improve patients' quality of life.