Yoshihiro Sakai, Shunsuke Kasai, Akio Shiomi
et al.
ABSTRACT Aim Few reports have described pelvic reinforcement procedure (PRP) to prevent perineal hernia (PH) in robotic abdominoperineal resection (Ro‐APR) for rectal cancer. This study aimed to investigate the safety and efficacy of PRP in Ro‐APR. Methods Patients who underwent Ro‐APR for rectal cancer between January 2020 and June 2023 were retrospectively examined. PRP was performed as a prophylactic procedure for PH. Four types of PRP were performed depending on the case (closure of the levator ani muscles, the pelvic peritoneum with the uterus, the pelvic peritoneum, and the pelvic peritoneum with a bladder peritoneal flap). Background factors and surgical outcomes were compared between patients without PRP (PRP−) and with PRP (PRP+). Imaged PH was diagnosed using computed tomography 1 year postoperatively. Imaged PH with symptoms was defined as symptomatic PH. Results We evaluated 81 patients, including 51 PRP− (63.0%) and 30 PRP+ (37.0%). There were no differences in the characteristics between the two groups. There was no significant difference in operative time between the two groups (358 min vs. 329 min, p = 0.460). PRP− had a significantly higher rate of imaged PH (39.2% vs. 6.7%, p = 0.005) and symptomatic PH (19.6% vs. 3.3%, p = 0.047). The two groups had no significant differences in the other postoperative complications. In multivariate analysis, the independent risk factor for PH was not undergoing PRP (odds ratio 9.71, p = 0.005). Conclusion PRP in Ro‐APR for rectal cancer can be safely performed and helps prevent PH.
Surgery, Diseases of the digestive system. Gastroenterology
Mark A Espeland, Sevil Yasar, Owen T Carmichael
et al.
ABSTRACT Objective We explore associations that four weight-sensitive and neuroactive cytokines – adiponectin, leptin, interleukin-6 (IL-6), and vascular endothelial growth factor (VEGF) – have with cross-sectional brain volumes among adults with obesity or overweight and with type 2 diabetes (T2D). Methods Cytokine concentrations were assayed at baseline and proximal to the end of two 10-year lifestyle interventions among 233 Look AHEAD trial participants. Magnetic resonance imaging measured total, white, and gray matter brain volumes. Associations that cytokine concentrations and changes in concentrations had with brain volumes and weight changes were assessed. Results Higher IL-6 and VEGF concentrations were associated with smaller total brain volumes (β=-3.00 [95% CI -14.82,-3.07] and β=-2.79 [-10.08,-1.74] log-units/cc respectively). Higher leptin concentrations were associated with higher white matter volumes (β=2.00 [-0.06,6.92] log-units/cc). Increases in adiponectin were associated with greater total brain volumes (β=2.14 [0.50,11.94] log-units/cc). Higher leptin and IL-6 concentrations were associated cross-sectionally with greater body mass index (BMI) and increases in leptin, IL-6, and VEGF concentrations were associated with increases in BMI. Neither lifestyle intervention nor changes in BMI materially affected associations between cytokines and brain volumes. Conclusions Weight-sensitive neuroactive cytokine concentrations are related to brain volumes. Weight changes and lifestyle interventions targeting weight loss may not materially influence these associations in adults with T2D.
INTRODUCTION: Gastrointestinal (GI) symptoms and weight loss develop during and after acute pancreatitis (AP), but remain understudied. In this prospective, multicenter study, we aim to assess GI symptom burden and weight loss and their correlation with exocrine function up to 12 months post-AP. METHODS: GI symptom burden, anthropometrics, and exocrine pancreatic function were systematically measured in adults (≥18 years) with AP at predefined intervals: hospitalization (enrollment), 3 months, and 12 months post-AP. Symptoms were evaluated using a 15-item tracker, including abdominal symptoms, stool characteristics, and activities of daily living, higher scores indicating greater symptom burden (range 0–45). Exocrine function was assessed with fecal elastase-1 (FE-1) levels. RESULTS: GI symptoms were collected in 97 participants with 12-month follow-up. The median (interquartile range) GI-symptom score was 7 (3–12) with 55 participants (57%) experiencing at least one symptom frequently (often or almost always). In multivariable linear regression, younger age, lower Charlson Comorbidity Index, smoking, recurrent AP, and alcoholic or idiopathic etiologies were associated with significantly higher GI-symptom burden at 12 months. A significant negative correlation was found between GI symptoms and FE-1 levels during hospitalization (ρ = −0.288; P = 0.015) and at 12 months (ρ = −0.219; P = 0.046). Eighteen participants (18.6%) lost ≥10% body weight between hospitalization and 12 months, and had significantly lower median FE-1 levels at 12 months compared with the group without weight loss (166 vs 332 µg/g, P = 0.016). DISCUSSION: This is the first study to prospectively assess GI-symptom burden and exocrine function post-AP. Lower exocrine pancreatic function at 12 months was associated with increased symptom burden and weight loss. These findings support further investigations to define and improve patient-reported outcomes post-AP. This study is registered with ClinicalTrials.gov, NCT03063398.
Takashi Taniguchi, Noboru Ideno, Tomoyuki Araki
et al.
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate owing to its late diagnosis and aggression. In addition, there are relatively few minimally invasive screening methods for the early detection of PDAC, making the identification of biomarkers for this disease a critical priority. Recent studies have reported that microRNAs in extracellular vesicles (EV‐miRs) from bodily fluids can be useful for the diagnosis of PDACs. Given this, we designed this study to evaluate the utility of cancer EVs extracted from duodenal fluid (DF) and their resident EV‐miRs as potential biomarkers for the detection of PDAC. Methods EV‐miRs were evaluated and identified in the supernatants of various pancreatic cancer cell lines (Panc‐1, SUIT2, and MIAPaca2), human pancreatic duct epithelial cells, and the DF from patients with PDAC and healthy controls. EVs were extracted using ultracentrifugation and the relative expression of EV‐miR‐20a was quantified. Results We collected a total of 34 DF samples (27 PDAC patients and seven controls) for evaluation and our data suggest that the relative expression levels of EV‐miR‐20a were significantly higher in patients with PDAC than in controls (p = 0.0025). In addition, EV‐miR‐20a expression could discriminate PDAC from control patients regardless of the location of the tumor with an area under the curve values of 0.88 and 0.88, respectively. Conclusions We confirmed the presence of EVs in the DF and suggest that the expression of EV‐miR‐20a in these samples may act as a potential diagnostic biomarker for PDAC.
Diseases of the digestive system. Gastroenterology
Mohammed A Alzahrani, Mohammed A Alfahadi, Meshref A Alshehri
et al.
Background: Esophageal motility disorders (EMDs) can significantly impact patients' quality of life. The Chicago Classification (CC) was developed as a robust framework to enable clinicians to better understand and classify the nature of motility disorders. Previous studies have primarily focused on the CC version 3.0 (CCv3.0), and data regarding the correlation between symptoms and CC version 4.0 (CCv4.0) in the Saudi Arabian population are lacking. This study aimed to assess the correlation between symptoms and CCv3.0 and CCv4.0 using high-resolution esophageal manometry (HRM) in Saudi Arabia, to evaluate the diagnostic performance of both classifications.
Methods: A total of 182 patients presenting with esophageal symptoms were included in this study. HRM was performed to assess esophageal motility, and patients' reported symptoms were recorded. The association between HRM findings and symptomatic variables was analyzed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results: Variability was observed in the diagnostic performance of symptomatic variables for major EMDs. CCv4.0 demonstrated a higher sensitivity for dysphagia than CCv3.0; however, it exhibited lower sensitivity to atypical gastroesophageal reflux disease (GERD) symptoms. Noncardiac chest pain (NCCP) exhibited the highest specificity and PPV, whereas typical GERD symptoms showed lower specificity.
Conclusion: CCv4.0 demonstrated potential improvements in sensitivity for dysphagia, but lower sensitivity for atypical GERD symptoms, compared with CCv3.0. These insights provide guidance for clinicians in Saudi Arabia and contribute to understanding the diagnostic performance of CCv3.0 and CCv4.0.
Diseases of the digestive system. Gastroenterology
Introduction Pancreatic portal hypertension (PPH) is a rare complication of acute pancreatitis (AP) that can lead to severe gastrointestinal bleeding. The risk factors associated with PPH, as well as the overall prognosis, warrant further investigation. This study aims to develop and validate a nomogram to predict PPH in patients with AP.Methods Consecutive patients with AP from 2015 to 2023 were retrospectively included in the study. Demographic data, clinical manifestations within the first week of AP onset, and initial contrast-enhanced CT findings were used to develop the predictive model. Univariate and multivariate Cox regression analyses were performed to identify risk factors for PPH. Based on the results of the multivariate analysis, a nomogram was developed. The patients were randomly divided into training and validation sets at a 7:3 ratio. The accuracy and discriminative power of the predictive model were assessed using the area under the curve (AUC) from the receiver operating characteristic curve and the calibration curve.Results Of the 1473 patients with AP, 107 (7.3%) developed PPH within 6 months (range: 2–22 months) during follow-up. Multivariate regression analysis showed that body mass index (BMI) (HR, 1.10; 95% CI 1.04 to 1.16; p=0.001), moderately severe grade (HR, 9.36; 95% CI 4.58 to 19.13; p<0.001), severe grade (HR, 12.95; 95% CI 6.22 to 26.94; p<0.001), diabetes (HR, 2.26; 95% CI 1.47 to 3.47; p<0.001), acute fluid accumulation (HR, 2.13; 95% CI 1.31 to 3.47; p=0.002), and necrosis (HR, 3.64; 95% CI 2.30 to 5.78; p<0.001) were independent risk factors for PPH. A nomogram for predicting PPH was developed, with the predictive curves showing an AUC of 0.859 at 6 months and 0.846 at 9 months. In the validation set, the AUC at both time points was 0.812.Conclusion In summary, we identified BMI, moderately severe or severe AP, diabetes, acute fluid accumulation, and necrosis as risk factors for AP-related PPH. Using the largest cohort of patients with AP to date, we developed a highly accurate nomogram with strong discriminative ability for predicting PPH. Future studies with larger sample sizes are necessary to confirm our findings and conduct external validation.
Diseases of the digestive system. Gastroenterology
Genetic diseases can be classified according to their modes of inheritance and their underlying molecular mechanisms. Autosomal dominant disorders often result from DNA variants that cause loss-of-function, gain-of-function, or dominant-negative effects, while autosomal recessive diseases are primarily linked to loss-of-function variants. In this study, we introduce a graph-of-graphs approach that leverages protein-protein interaction networks and high-resolution protein structures to predict the mode of inheritance of diseases caused by variants in autosomal genes, and to classify dominant-associated proteins based on their functional effect. Our approach integrates graph neural networks, structural interactomics and topological network features to provide proteome-wide predictions, thus offering a scalable method for understanding genetic disease mechanisms.
In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output.
The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face. These issues are addressed in this research by utilizing machine learning methods and real-world datasets. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors. These datasets offer insightful information on disease trends, soil nutrition demand of crops, and agricultural production history. By incorporating this knowledge, the model first recommends the list of primarily selected crops based on the soil nutrition of a particular user location. Then the predictions of meteorological variables like temperature, rainfall, and humidity are made using SARIMAX models. These weather predictions are then used to forecast the possibilities of diseases for the primary crops list by utilizing the support vector classifier. Finally, the developed model makes use of the decision tree regression model to forecast crop yield and provides a final crop list along with associated possible disease forecast. Utilizing the outcome of the model, farmers may choose the best productive crops as well as prevent crop diseases and reduce output losses by taking preventive actions. Consequently, planning and decision-making processes are supported and farmers can predict possible crop yields. Overall, by offering a detailed decision support system for crop selection and disease prediction, this work can play a vital role in advancing agricultural practices in Bangladesh.
The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure.
Christian Mouawad, Rany Aoun, Houssam Dahboul
et al.
Introduction: The negative impact of obesity on the quality of life (QoL) and its association with multiple comorbidities is unquestionable. The primary objective of this study was to compare the QoL of patients before, 1 year and 5 years after laparoscopic sleeve gastrectomy (LSG). Secondary objectives were to evaluate the resolution of obesity-related comorbidities and weight loss success.
Materials and Methods: We included patients who underwent LSG for body mass index (BMI) ≥30 kg/m2 between August 2016 and April 2017 and completed the Moorehead-Ardelt QoL Questionnaire II (MA II). Statistical analysis was conducted using SPSS IBM Statistics for Windows version 21.
Results: In total, 64 patients participated with a female majority (73.44%) and a mean age of 36.09 with an average BMI at 40.47. Percentage of excess BMI loss and excess weight loss (% EWL) at one and 5 years after surgery went from 90.18% to 85.05% and 72.17% to 67.09%, respectively. The total MA II score before LSG was − 0.39 ± 0.94. Postoperatively, it increased to 1.73 ± 0.60 at 1 year and 1.95 ± 0.67 at 5 years. The positive impact of LSG on QoL was more significant in patients presenting ≥30% of weight loss and in females. At 5 years, a significant improvement in many comorbidities was noted except for arterial hypertension, coxalgia, gastro-oesophageal reflux disease and lower extremities' varices.
Conclusion: LSG maintains a long-term QoL improvement, a significant EWL and a resolution of the most common obesity-associated comorbidities such as diabetes, dyslipidaemia and symptoms related to sleep apnoea.
Surgery, Diseases of the digestive system. Gastroenterology
Ritesh Chandra, Sadhana Tiwari, Sonali Agarwal
et al.
Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the mortality rate may rise. In this work, we propose a set of ontologies that will help in the diagnosis and treatment of vector-borne diseases. For developing VBD's ontology, electronic health records taken from the Indian Health Records website, text data generated from Indian government medical mobile applications, and doctors' prescribed handwritten notes of patients are used as input. This data is then converted into correct text using Optical Character Recognition (OCR) and a spelling checker after pre-processing. Natural Language Processing (NLP) is applied for entity extraction from text data for making Resource Description Framework (RDF) medical data with the help of the Patient Clinical Data (PCD) ontology. Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment. The developed ontology helps in the construction of decision support systems (DSS) for the NVBDCP to control these diseases.
Utkarsh Yashwant Tambe, A. Shobanadevi, A. Shanthini
et al.
In this study, a Convolutional Neural Network (CNN) is used to classify potato leaf illnesses using Deep Learning. The suggested approach entails preprocessing the leaf image data, training a CNN model on that data, and assessing the model's success on a test set. The experimental findings show that the CNN model, with an overall accuracy of 99.1%, is highly accurate in identifying two kinds of potato leaf diseases, including Early Blight, Late Blight, and Healthy. The suggested method may offer a trustworthy and effective remedy for identifying potato diseases, which is essential for maintaining food security and minimizing financial losses in agriculture. The model can accurately recognize the various disease types even when there are severe infections present. This work highlights the potential of deep learning methods for categorizing potato diseases, which can help with effective and automated disease management in potato farming.