Vascular Surgery Procedures Performed By Residents. A Narrative Review On The Impact In 30-Day Outcomes.

INTRODUCTION: Worldwide, there is an increase in scrutiny after surgical treatment of a vast array of pathologies. Doing so, a large body of evidence clearly supports centralisation, such as teaching hospitals, where a larger caseload enables optimal outcomes. These institutions have a strong presence of surgical residents seeking training in both technical and non-technical skills. Inevitably, as part of training, they will be involved in the surgical treatment of those patients, even as the primary operator. We sought to investigate the impact of trainee performed procedures in outcomes of common vascular procedures of different technical complexity. METHODS: A non-systematic MEDLINE and Scopus databases review on the outcomes of resident performed common vascular procedures was performed. RESULTS: Specific evidence in many procedures (venous disease, aortic aneurysms, peripheral artery disease) is lacking. After carotid endarterectomy (CEA), resident performed procedures seem to have similar cranial nerve palsy and stroke when compared to expert surgeons. Generally, resident-performed primary radiocephalic and elbow arteriovenous fistula (AVF) presents similar primary and secondary patency. As with CEA, AVF procedures performed by residents took longer. On aortic aneurysms, although no specific comparison has been performed, resident involvement (irrespective of surgeon or assistant) in these procedures does not seem associated with increased adverse events. CONCLUSION: In most vascular surgery procedures, little is known about resident performance and their impact on outcomes. Notwithstanding, resident-performed CEA and primary AVF seem free of major compromise to patients. Further research is warranted to clarify this topic.

A case of equine multicentric lymphoma: Clinical, microscopical, and molecular findings.

Background: Although relatively uncommon, lymphoma is the most prevalent haematopoietic neoplasia in horses, and multicentric lymphoma remains the most common presentation of the disease. The pathogenesis of equine lymphoma is still poorly understood and the diagnosis is usually confirmed at an advanced stage of the disease, compromising the prognosis. This study investigated the clinical, pathological, and molecular features of a case of equine multicentric lymphoma. Case Description: An apparently healthy 5-year-old crossbreed mare hospitalized at the Centre of Animal Reproduction of Vairão, Portugal, suddenly presented clinical signs of supraorbital oedema and mandibular lymph node enlargement, developing fever, facial oedema, and generalized lymphadenopathy. The mare ended up dying twenty-four days after the first clinical signs due to multisystem organ failure. Haematological and biochemical analyses, necropsy, and microscopic and molecular evaluation of affected tissues were performed. At necropsy, the main findings were multiple multinodular lesions, distributed along the serous surface of oropharynx, trachea, pericardium, gastrointestinal tract, and mesentery. Microscopically, these consisted of solid proliferations of neoplastic round cells that exhibited immunopositivity for CD3 (T cells). Based on these findings, a medium-grade multicentric T-cell lymphoma was diagnosed. Conclusion: There is still very little research regarding the molecular characterization of lymphoma in horses. As an entity itself is quite heterogeneous, it is important to describe the interspecies particularities to understand its development and behaviour.

Recombinant inbred line panels inform the genetic architecture and interactions of adaptive traits in Drosophila melanogaster.

The distribution of allelic effects on traits, along with their gene-by-gene and gene-by-environment interactions, contributes to the phenotypes available for selection and the trajectories of adaptive variants. Nonetheless, uncertainty persists regarding the effect sizes underlying adaptations and the importance of genetic interactions. Herein, we aimed to investigate the genetic architecture and the epistatic and environmental interactions involving loci that contribute to multiple adaptive traits using two new panels of Drosophila melanogaster recombinant inbred lines (RILs). To better fit our data, we re-implemented functions from R/qtl (Broman et al. 2003) using additive genetic models. We found 14 quantitative trait loci (QTL) underlying melanism, wing size, song pattern, and ethanol resistance. By combining our mapping results with population genetic statistics, we identified potential new genes related to these traits. None of the detected QTLs showed clear evidence of epistasis, and our power analysis indicated that we should have seen at least one significant interaction if sign epistasis or strong positive epistasis played a pervasive role in trait evolution. In contrast, we did find roles for gene-by-environment interactions involving pigmentation traits. Overall, our data suggest that the genetic architecture of adaptive traits often involves alleles of detectable effect, that strong epistasis does not always play a role in adaptation, and that environmental interactions can modulate the effect size of adaptive alleles.

Antitussive, Expectorant and Antipyretic Effect of the Ethanolic Extract of the Leaves of Momordica charantia L.

The traditional use of the M. charantia L. plant to treat coughs, fever and expectoration is widely practiced in different cultures, but its effectiveness and safety still require scientific investigation. This study sought to perform a chemical analysis and evaluate the antitussive, expectorant and antipyretic effects of the ethanolic extract of M. charantia leaves (EEMc) in rats and mice. The EEMc was subjected to chemical analysis by HPLC-DAD, revealing the presence of the flavonoids astragalin and isoquercetin. Acute oral toxicity in mice did not result in deaths, although changes in liver weight and stool consistency were observed. EEMc demonstrated an antitussive effect at doses of 100 and 300 mg/kg in mice subjected to cough induction by citric acid nebulization. Furthermore, it showed expectorant activity at a dose of 300 mg/kg, assessed based on the elimination of the phenol red marker in bronchoalveolar lavage. In the evaluation of antipyretic activity in rats, fever induced by Saccharomyces cerevisiae was reduced at all doses tested during the first hour after treatment. This innovative study identified the presence of astragalin and isoquercetin in EEMc and indicated that the extract has antitussive, expectorant and antipyretic properties. Therefore, EEMc presents itself as a promising option in herbal medicine for the treatment of respiratory symptoms and fever.

Factors Associated With Loss to Follow-Up in Surgical Trials: A Systematic Review and Meta-Analysis.

INTRODUCTION: Loss to follow-up (LTFU) distorts results of randomized controlled trials (RCTs). Understanding trial characteristics that contribute to LTFU may enable investigators to anticipate the extent of LTFU and plan retention strategies. The objective of this systematic review and meta-analysis was to investigate the extent of LTFU in surgical RCTs and evaluate associations between trial characteristics and LTFU. METHODS: MEDLINE, Embase, and PubMed Central were searched for surgical RCTs published between January 2002 and December 2021 in the 30 highest impact factor surgical journals. Two-hundred eligible RCTs were randomly selected. The pooled LTFU rate was estimated using random intercept Poisson regression. Associations between trial characteristics and LTFU were assessed using metaregression. RESULTS: The 200 RCTs included 37,914 participants and 1307 LTFU events. The pooled LTFU rate was 3.10 participants per 100 patient-years (95% confidence interval [CI] 1.85-5.17). Trial characteristics associated with reduced LTFU were standard-of-care outcome assessments (rate ratio [RR] 0.17; 95% CI 0.06-0.48), surgery for transplantation (RR 0.08; 95% CI 0.01-0.43), and surgery for cancer (RR 0.10; 95% CI 0.02-0.53). Increased LTFU was associated with patient-reported outcomes (RR 14.21; 95% CI 4.82-41.91) and follow-up duration ≥ three months (odds ratio 10.09; 95% CI 4.79-21.28). CONCLUSIONS: LTFU in surgical RCTs is uncommon. Participants may be at increased risk of LTFU in trials with outcomes assessed beyond the standard of care, surgical indications other than cancer or transplant, patient-reported outcomes, and longer follow-up. Investigators should consider the impact of design on LTFU and plan retention strategies accordingly.

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study.

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Deep learning and minimally invasive inflammatory activity assessment: a proof-of-concept study for development and score correlation of a panendoscopy convolutional network.

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE. Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation. Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated. Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS). Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB_AIS was 0.5659 (0-29.45). We found a strong correlation between SB_AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r = 0.751, r = 0.707, r = 0.655, p = 0.001). We found a strong correlation between LS and ELIAKIM (r = 0.768, p = 0.001) and a very strong correlation between CECDAI and LS (r = 0.854, p = 0.001) and CECDAI and ELIAKIM scores (r = 0.827, p = 0.001). Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients.

Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation.

Deep learning and capsule endoscopy: Automatic multi-brand and multi-device panendoscopic detection of vascular lesions.

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings.

Patients with complete clinical response after neoadjuvant chemoradiotherapy for locally advanced esophageal cancer: A Markov decision analysis of esophagectomy versus active surveillance.

OBJECTIVE: Chemoradiation followed by esophagectomy is a standard treatment option for patients with locally advanced esophageal cancer (LAEC). Esophagectomy is a high-risk procedure, and recent evidence suggests select patients may benefit from omitting or delaying surgery. This study aims to compare surgery versus active surveillance for LAEC patients with complete clinical response (cCR) after neoadjuvant chemoradiotherapy (nCRT). METHODS: Decision analysis with Markov modeling was used. The base case was a 60-year-old man with T3N0M0 esophageal cancer with cCR after nCRT. The decision was modeled for a 5-year time horizon. Primary outcomes were life-years and quality-adjusted life-years (QALY). Probabilities and utilities were derived through the literature. Deterministic sensitivity analyses were performed using ranges from the literature with consideration for clinical plausibility. RESULTS: Surgery was favored for survival with an expected life-years of 2.89 versus 2.64. After incorporating quality of life, active surveillance was favored, with an expected QALY of 1.70 versus 1.56. The model was sensitive to probability of recurrence on active surveillance (threshold value 0.598), probability of recurrence being resectable (0.318), and disutility of previous esophagectomy (-0.091). The model was not sensitive to perioperative morbidity and mortality. CONCLUSIONS: Our study finds that surgery increases life expectancy but decreases QALY. Although the incremental change in QALY for either modality is insufficient to make broad clinical recommendations, our study demonstrates that either approach is acceptable. As probabilities of key factors are further defined in the literature, treatment decisions for patients with LAEC and a cCR after nCRT should consider histology, patient values, and quality of life.

The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality.

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches-scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent, χ = 2 , reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally, χ is significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restores χ = 2 , even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifies χ = 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common 'crackling noise' approach fails to determine χ under similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.

Performance of Intracystic Glucose Measurement for the Characterization of Pancreatic Cystic Lesions.

BACKGROUND AND AIMS: Endoscopic ultrasound (EUS)-guided fine-needle aspiration (FNA) is essential for the classification of pancreatic cystic lesions (PCLs). Recently, intracystic glucose has been suggested as an alternative to carcinoembryonic antigen (CEA) level as a predictor of mucinous cystic lesions (M-PCLs). This study aims to evaluate the diagnostic performance of intra-cystic glucose in distinguishing between M-PCLs and non M-PCLs (NM-PCLs) and to analyze the possibility of on-site glucose measurement with a standard glucometer. METHODS: Patients with PCLs submitted to EUS-FNA with simultaneous intracystic glucose measurement between 2017 and 2022 were included. The diagnostic performance of glucose versus CEA for the differentiation between M-PCLs and NM-PCLs was compared to a final diagnosis based on the analysis of surgical specimen, intracystic biopsy or, if this data was unavailable, multidisciplinary evaluation. A cut-off of <50 mg/dL was used for the diagnosis of MCLs. Additionally, the agreement between on-site glucose determination with a standard glucometer and laboratory glucose measurement was assessed. RESULTS: Mucinous lesions accounted for 56% of all PCLs. The median values of glucose and CEA for M-PCLs were 18 mg/dL and 286 ng/mL, respectively. Intracystic glucose had a sensitivity and specificity of 93.2% and 76.5%, respectively, for the diagnosis of MCLs (versus 55.6% and 87.5%, respectively, for CEA). The area under the curve was 0.870 for on-site glucose (versus 0.806 for CEA). An excellent correlation was observed between on-site and laboratory glucose measurement (ρ=0.919). CONCLUSIONS: The measurement of intracystic glucose showed superior performance compared with CEA in distinguishing between M-PCLs and NM-PCLs, with excellent correlation between on-site and conventional lab glucose measurement. Thus, on-site intracystic glucose appears to be an excellent biomarker for the characterization of PCLs due to its low cost, high availability, and the need for a minimal cyst fluid volume for its determination.

Does Portal Hypertension Increase the Risk of Helicobacter pylori Infection and Pre-Malignant Gastric Lesions?

Background and Aims: The presence of portal hypertension in cirrhotic patients is a major prognostic factor associated with the development of severe complications and increased mortality. The gold standard for diagnosing portal hypertension is the hepatic venous pressure gradient. More recently, spleen stiffness has emerged as a new and non-invasive diagnostic tool, and has already been included in the last Baveno VII guidelines. The exact prevalence of Helicobacter pylori infection, pre-malignant lesions and their relation to portal hypertension have never been described. The aim of our study was to evaluate the relationship between the presence of portal hypertension assessed via liver and spleen elastography and Helicobacter pylori infection and pre-malignant gastric lesions. Methods: An observational study was conducted, including consecutive patients admitted from December 2020 to December 2022. All patients underwent upper endoscopy and were also subjected to liver and spleen elastography (using the new probe of 100 Hz) by the same blinded operator in a tertiary center. Results: We included 155 cirrhotic patients, with a mean age of 64.1 years (±8.8), and 81.3% were male. The most common etiology was alcoholic liver disease (72.9%). The median value of liver stiffness measurement was 24.4 kPa [3.1-75.0], and the spleen stiffness measurement was 49.1 kPa [12.8-100.0]. Akin to endoscopic findings, 50.3% presented esophageal varices, 5.2% gastric atrophy, 11.6% gastric metaplasia, and 32.9% portal hypertension gastropathy. Regarding histologic findings, we found that 34.8% presented H. pylori infection, 35.5% gastric atrophy (OLGA 1-58.2%) and 38.7% gastric metaplasia (OLGIM 1-63.3%). Liver stiffness and spleen stiffness measurements were associated with the presence of portal hypertensive gastropathy (p < 0.01), but not with H. pylori infection or pre-malignant gastric lesions. Conclusions: Although present in almost one third of cirrhotic patients, H. pylori infection and pre-malignant gastric lesions are not associated with liver stiffness and spleen stiffness measurements. On the other hand, we found an association between liver stiffness and spleen stiffness measurements and portal hypertensive gastropathy.

Grip strength buffers the harmful association between multimorbidity and depression among middle-aged and older adults.

BACKGROUND: Grip strength (GS) is associated to both multimorbidity and depression, however its possible moderating effect is unknown. This study aimed to investigate GS moderating effect on the association between multimorbidity and depression. METHODS: Data from SHARE wave 8 was used. Participant were 41457 middle-aged and older adults (17954 men) from 18 European countries. A regression analysis was conducted for the moderating effect of sex- and age-specific GS quartiles (W) on the association between number of chronic diseases (X1) or multimorbidity (X2) and depression symptoms (Y). RESULTS: More chronic diseases were associated with greater depressive symptomatology (men: B = 0.39, 95 % CI: 0.35, 0.42; women: B = 0.42, 95 % CI: 0.39, 0.45). On the other hand, being in a higher GS quartile was associated with fewer depression symptoms, and this association was stronger the higher the quartile was. Having a higher GS represented a decrease in depression symptoms associated with multimorbidity for men (quartile 1: B = 0.85, 95 % CI = 0.74, 0.95 vs. quartile 4: B = 0.49, 95 % CI = 0.38, 0.61) and women (quartile 1: B = 1.08, 95 %CI = 0.97, 1.19 vs. quartile 4: B = 0.59, 95 %CI: 0.47, 0.70). CONCLUSIONS: Strategies aiming to reduce the impact of multimorbidity on mental health should promote muscle-strengthening physical activity among middle-aged and older adults.

Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks.

We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.

Trial-by-trial variability in cortical responses exhibits scaling of spatial correlations predicted from critical dynamics.

In the mammalian cortex, even simple sensory inputs or movements activate many neurons, with each neuron responding variably to repeated stimuli-a phenomenon known as trial-by-trial variability. Understanding the spatial patterns and dynamics of this variability is challenging. Using cellular 2-photon imaging, we study visual and auditory responses in the primary cortices of awake mice. We focus on how individual neurons' responses differed from the overall population. We find consistent spatial correlations in these differences that are unique to each trial and linearly scale with the cortical area observed, a characteristic of critical dynamics as confirmed in our neuronal simulations. Using chronic multi-electrode recordings, we observe similar scaling in the prefrontal and premotor cortex of non-human primates during self-initiated and visually cued motor tasks. These results suggest that trial-by-trial variability, rather than being random noise, reflects a critical, fluctuation-dominated state in the cortex, supporting the brain's efficiency in processing information.

Differential amplification of the subtelomeric satellite DNA JcSAT1 in the genus Jatropha L. (Euphorbiaceae).

Satellite DNAs (satDNAs) are highly repetitive sequences that occur in virtually all eukaryotic genomes and can undergo rapid copy number and nucleotide sequence variation among relatives. After chromosomal mapping of the satDNA JcSAT1, it was found a large accumulation at subtelomeres of Jatropha curcas (subgenus Curcas), but an absence of these monomers in J. integerrima (subgenus Jatropha). This fact suggests a dynamic scenario for this satellite repeat in Jatropha genomes. Here, we used a multitasking approach (sequence analysis, DNA blotting and chromosomal mapping) to investigate the molecular organization and chromosomal abundance and distribution of JcSAT1 in a broader group of species from the subgenus Jatropha (J. gossypiifolia, J. mollissima, J. podagrica, and J. multifida) in addition to J. curcas, with the aiming of understanding the evolution of this satDNA. Based on the analysis of BAC clone sequences of J. curcas, a large array (~ 30 kb) of 80 homogeneous monomers of JcSAT1 was identified in BAC 23J11. The monomer size was conserved (~ 358 bp) and contained a telomeric motif at the 5' end. PCR amplification coupled with a Southern blot revealed the presence of JcSAT1-like sequences in all species examined. However, a large set of genome copies was identified only in J. curcas, where a ladder-like pattern with multimers of different sizes was observed. In situ hybridization of BAC 23J11 confirmed the subtelomeric pattern for J. curcas, but showed no signals on chromosomes of species from the subgenus Jatropha. Our data indicate that JcSAT1 is a highly homogeneous satDNA that originated from a region near the telomeres and spread throughout the chromosomal subtermini, possibly due to frequent ectopic recombination between these regions. The abundance of JcSAT1 in the genome of J. curcas suggests that an amplification event occurred either at the base of the subgenus Curcas or at least in this species, although the repeat is shared by all species of the genus studied so far.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.