Complete Data Research Articles

Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis

BACKGROUND Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis. AIM To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis. METHODS In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training (n = 145) and validation (n = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus–gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort. RESULTS The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692. CONCLUSION In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.

Younger age, longer delay to surgery and meniscal tears are associated with a smaller ACL remnant: An analysis from the registry of the Francophone Arthroscopic Society

AbstractPurposeThis study aimed to evaluate which preoperative patient, injury or clinical factors were associated with the anterior cruciate ligament (ACL) remnant volume in patients undergoing ACL surgery. It was hypothesized that the main factors determining an insufficient ACL remnant volume at the time of surgery were younger age and longer time to surgery.MethodsA retrospective analysis from the Francophone Arthroscopic Society's registry was conducted, including 1565 patients with an ACL lesion underdoing a primary ACL surgery (reconstruction or repair) between June 2020 and June 2023. Patients were excluded in case of revision surgery and incomplete data. Preoperative factors—including patient demographics, delay to surgery, preoperative laxity and the presence of meniscal tears or cartilage lesions—were analysed to determine their influence on ACL remnant volume (estimated by the surgeon as the percentage of residual volume). Univariate, multivariate and receiver operating characteristic curve analyses were performed to explore these relationships.ResultsMultivariate analyses demonstrated that younger age (<20 years and 20–30 years compared to ≥40, p = 0.02), higher time from injury to surgery (≥12 months compared to <3 months, p = 0.01) and the presence of a medial (p = 0.01) or a lateral meniscal tear (p = 0.02) were significant predictors of an ACL remnant volume ≤ 50%.ConclusionsYounger age (under 30 years of age), a time from injury to surgery above 12 months and the presence of medial and lateral meniscal tears are associated with higher odds of observing a smaller ACL remnant volume at the time of the ACL surgery. These factors should be considered when planning ACL remnant preservation techniques.Level of evidenceLevel III.

A needs assessment for simulation‐based training in emergency urological skills in Ethiopia

AbstractBackgroundUrologic emergencies are common and complications from their management are high. Simulation‐based education (SBE) is a highly effective training method, allowing trainees to learn both technical and nontechnical skills in a safe environment. Training in the management of urological emergencies is limited in many healthcare settings, so we performed a needs assessment for a urological emergencies SBE course in Ethiopia.MethodsThis study presents data from a descriptive cross‐sectional, survey‐based survey of consultants and residents in the Ethiopian surgical community. The survey was disseminated using online Google Forms, through social media (WhatsApp), and to colleagues in the College of Surgeons of East, Central and Southern Africa (COSECSA) via email and social media.ResultsOne hundred‐seven results were received; two were discarded due to incomplete data. Fifty three of the respondents were general surgeons and 38 of the respondents were urologists. Sixty nine respondents strongly agreed that simulation‐based training was important for first‐year surgical residents, whereas twenty‐five respondents agreed and nine respondents strongly disagreed; one respondent disagreed and one was neutral. Eighty seven respondents suggested a 3‐day training course, whereas 17 respondents suggested a two‐day course. More than 80 of the respondents rated training in the management of acute urinary retention, acute scrotum, urethral, and suprapubic catheterization as extreme or very important and 79 respondents wanted education about urologic trauma and Fournier's gangrene.ConclusionSurgical and urology residents in Ethiopia have expressed a need for, and a strong interest in, simulation‐based urological emergency training.

Risk assessment for femoral head collapse in osteonecrosis utilizing MRI-derived large lesion ratio: a retrospective cohort study

PurposeThis study aimed to developed a novel and practical method to quantify the involvement of lesion in osteonecrosis of the femoral head (ONFH). We hypothesized that the new metric large lesion ratio (LLR) had promising prognostic value.MethodsA total of 131 hips with non-traumatic ONFH were included in this retrospective study. Patient aged 18–60 with MRI-confirmed diagnosis, and a minimum of 2-year follow-up or radiographic collapse progression during follow-up were included. Patients with prior hip surgery, incomplete data or advanced ONFH at baseline (femoral head collapse > 2 mm or osteoarthritis) were excluded. Involvement of necrotic lesion was evaluated by calculating LLR. The differences of LLR between collapse progression and non-progression groups were investigated, and the differences among different scanning parameters groups were also examined. Prognostic value of LLR was examined by multivariate regression analysis. Receiver operating characteristic curves (ROC) were constructed and areas under the curve (AUC) were compared.ResultsThe median of LLR was 66.67% in the collapse progression group, which was significantly higher compared with 25.00% in the non-progression group (P < 0.001). Subgroups analysis showed that LLR were significantly higher in the collapse progression group of Japanese Investigation Committee type C1 (P < 0.001)and C2 (P = 0.002). Multivariate regression showed that LLR were independently correlated with collapse progression (OR, 1.46 [95% CI, 1.24–1.78]; P < 0.001). ROC analysis showed that the AUC for LLR was 0.84, outperforming the 0.74 AUC OF the JIC classification.ConclusionLLR could served as a efficient tool to assess the risk of collapse progression and guide the selection of treatment strategy.

A hybrid tracking method for maritime obstacles using sensor data

Safe navigation of ships depends on the accurate recognition and tracking of nearby maritime obstacles as detected by various sensors. Challenges arise in tracking maritime obstacles from sensor data because of sensor noise and incomplete sensor data. Traditionally, tracking algorithms such as the EKF (Extended Kalman Filter) have been applied to track the state of maritime obstacles, including the position, COG (Course Over Ground), and SOG (Speed Over Ground). This study implemented a combined EKF- and learning-based (hybrid) tracking method. In the EKF-based method, the parameters are related to the uncertainty of the system and the sensor data. These parameters are generally set manually by analyzing the noise of the sensor data and may not be optimal; we optimized the parameters to compensate for this. In the learning-based method, we trained a deep learning model using a DNN (Deep Neural Network) to predict obstacle states from sensor measurement data. We then propose a hybrid tracking method that combines the two tracking methods to compensate for the shortcomings of each method. We verified these three tracking methods using navigation data obtained through field tests. The verification results showed that the learning-based tracking method improved the SOG tracking accuracy by 11.47% compared with the traditional EKF-based tracking method. The tracking accuracy of the hybrid tracking method was reduced by 22.42% for the COG and 42.05% for the SOG. These results indicate that the hybrid tracking method effectively compensates for the limitations of the other methods, resulting in an enhanced tracking performance.

Estimation of dynamic Origin–Destination matrices in a railway transportation network integrating ticket sales and passenger count data

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

Pregnancy Related Medication Prescription Patterns

Introduction: Drug use during pregnancy presents significant challenges due to altered pharmacokinetics and pharmacodynamics, with potential adverse effects on the fetus. The USFDA (United States Food and Drug Administration) categorizes drugs from A to X based on their safety in pregnancy, guiding appropriate drug use. Hence, this research aimed to assess rationality in prescription pattern and to identify commonly prescribed medications in pregnant women. Methodology: A pilot prospective observational study was conducted for three months at Gandhi Hospital, Secunderabad. Women of any gestational age willing to participate voluntarily were included in study while non-pregnancy cases and cases with incomplete data were excluded. Data collection focused on demographics, medical history, and prescribed medications. Prescription pattern was analyzed using WHO (World Health Organization) core prescribing indicators and USFDA pregnancy drug-risk categories. Results: Study highlighted the prevalence of anemia in 50% of pregnant women making it a possible factor for complications like premature birth and low birth weight if untreated. Majority of prescribed medications belong to Category B. Among 70 prescriptions, average number of drugs per prescription was 5.857 which was excess. Supplemental drug use accounted for 40.97% of the 410 prescription drugs, 168 drugs, and 242 drugs (59.024%) of the non-supplemental drugs prescribed. Conclusion: Study emphasizes importance of supplemental drug use which was found to be deficient in the prescriptions. It is necessary for the well-being of mother and growth of fetus. Findings provide healthcare professionals with insights into current prescribing practices, aiming to enhance rational drug use in pregnancy. Keywords: Pregnancy; USFDA drug risk category; prescription pattern.

In Vitro Bond Strength of Dentin Treated with Sodium Hypochlorite: Effects of Antioxidant Solutions

This systematic review aims to evaluate whether the application of antioxidant solutions can enhance the bond strength of resin-based materials to sodium hypochlorite (NaOCl)-treated dentin. This study follows the PICOT strategy: population (sodium hypochlorite-treated dentin), intervention (application of antioxidants), control (distilled water), outcome (bond strength), and type of studies (in vitro studies). The systematic review and meta-analysis were conducted following PRISMA guidelines. Electronic databases were searched for in vitro studies evaluating the effects of antioxidants on bond strength to sodium hypochlorite-treated dentin. Two independent reviewers screened articles, extracted data, and assessed risk of bias. Meta-analyses were performed using a random-effects model to compare standardized mean differences in bond strength between antioxidant pretreatment and control groups. Inclusion criteria consisted of in vitro studies that examined the bond strength of resin-based materials to NaOCl-treated dentin with antioxidant application, while exclusion criteria included studies with incomplete data, those not using a control group, or those that did not directly measure bond strength. From 3041 initial records, 29 studies were included in the qualitative analysis and 25 in the meta-analysis. Ascorbic acid, sodium ascorbate, grape seed extract, green tea, and rosmarinic acid significantly improved bond strength to sodium hypochlorite-treated dentin (p &lt; 0.05). The effectiveness of grape seed extract varied with adhesive system type. Hesperidin, p-toluene sulfonic acid, and sodium thiosulfate did not significantly improve bond strength. Most studies had a high risk of bias. This suggests that the conclusions drawn from these studies should be interpreted with caution, and further research with more robust methodologies may be needed to confirm the findings. In conclusion, this systematic review implies that certain antioxidants can improve bond strength to sodium hypochlorite-treated dentin, with efficacy depending on the specific agent and adhesive system used. Further standardized studies are needed to optimize protocols and confirm these findings.

Tensor-based unsupervised feature selection for error-robust handling of unbalanced incomplete multi-view data

Recent advancements in multi-view unsupervised feature selection (MUFS) have been notable, yet two primary challenges persist. First, real-world datasets frequently consist of unbalanced incomplete multi-view data, a scenario not adequately addressed by current MUFS methodologies. Second, the inherent complexity and heterogeneity of multi-view data often introduce significant noise, an aspect largely neglected by existing approaches, compromising their noise robustness. To tackle these issues, this paper introduces a Tensor-Based Error Robust Unbalanced Incomplete Multi-view Unsupervised Feature Selection (TERUIMUFS) strategy. The proposed MUFS framework specifically caters to unbalanced incomplete multi-view data, incorporating self-representation learning with a tensor low-rank constraint and sample diversity learning. This approach not only mitigates errors in the self-representation process but also corrects errors in the self-representation tensor, significantly enhancing the model’s resilience to noise. Furthermore, graph learning serves as a pivotal link between MUFS and self-representation learning. An innovative iterative optimization algorithm is developed for TERUIMUFS, complete with a thorough analysis of its convergence and computational complexity. Experimental results demonstrate TERUIMUFS’s effectiveness and competitiveness in addressing unbalanced incomplete multi-view unsupervised feature selection (UIMUFS), marking a significant advancement in the field.

Immediate and short-term outcomes of percutaneous balloon mitral commissurotomy for severe mitral stenosis at a Resource Limited Setting, Addis Ababa, Ethiopia

BackgroundRheumatic heart disease is a common cause of mitral stenosis (MS) in developing nations. Percutaneous mitral balloon commissurotomy (PMBC) is the treatment of choice for patients with MS with favorable anatomy. ObjectiveThis study aimed to assess immediate and short-term outcomes of percutaneous balloon mitral commissurotomy for symptomatic patients with rheumatic severe mitral stenosis. MethodsThe clinical records of patients who underwent Percutaneous Mitral Balloon Commissurotomy (PBMC) at Saint Paul Hospital Millennium Medical College, Cardiac Center were reviewed. A retrospective observational study was conducted from February 01, 2018, to August 30, 2021. All eligible patients who underwent Percutaneous Mitral Balloon Commissurotomy during the study period at the cardiac center were included in this study. Ninety-one patients were enrolled in the study after excluding repeat procedures, those with incomplete data, and lost medical records. Data were collected through chart reviews using a structured questionnaire by trained physicians, with daily supervision by the investigators. Data analysis was performed using SPSS version 25. Associations were checked using a paired t-test and Fisher's Exact Test, with statistical significance set at a p-value of <0.05. ResultPercutaneous mitral balloon commissurotomy was successful in 91.2 % of patients. Following the procedure, there was a significant increase in the mean mitral valve area from 0.799 cm2 to 1.674 cm2 and a reduction in the trans-mitral mean pressure gradient from 20 mmHg to 7.71 mmHg (P < 0.001). Atrial fibrillation and a high Wilkins score were identified as correlates of non-successful PMBC. A Wilkin's score of ≤8 and a post-procedure mitral valve area > 1.5 cm2 were associated with New York Heart Association functional class improvement three months after the procedure. ConclusionIn this study, percutaneous mitral balloon Commissurotomy (PMBC) demonstrated an excellent success rate. The majority of patients exhibited significant improvement in the New York Heart Association (NYHA) functional class.

Decompressive nerve surgery associated with low diabetic foot ulcer rate and high-risk bias: A systematic review and meta-analysis.

Diabetic sensorimotor polyneuropathy (DSPN) occurs in 50% of diabetes mellitus (DM) patients. Up to 70% of DM patients incur diabetic foot ulcer(s) (DFU). Of those with DSPN, 20% develop DFU and medical therapy remains challenging. Decompression nerve surgery (DNS) purportedly improves DSPN and DFU. Currently, there is no accurate, pooled DFU incidence post-DNS. Thus, this systematic review meta-analyses DFU incidence post-DNS. Given the 60% global DFU incidence, we hypothesised a lower, pooled DFU incidence following DNS. Two authors searched six databases (MEDLINE/PubMed/Web of Science/Embase/Scopus/Cochrane/Google Scholar) to July 2023 for DNS clinical studies. Primary tracked outcomes were new and recurrent DFU incidence post-DNS. Inclusion criteria: clinical studies, adult DM patients, DNS of tibial nerve(s) at minimum, three-month minimum follow-up, and reported new/recurrent DFU incidence post-DNS. Exclusion criteria: non-clinical, non-human studies. GRADE and MINORS instrument(s) evaluated evidence. With random-effects model, DFU incidence was meta-analysed. For inclusion, 1074 studies were identified and 208 screened. Thirteen studies yielded 2466 and 540 new and recurrent patients with DFU incidence(s) of 1% and 5%, respectively. Primary studies had low-level evidence and high-risk bias, including selection bias using Cochrane Collaboration Bias Risk Tool. A sub-analysis of sample size (new:0% and recurrent:2%) and follow-up length (new:1% and recurrent:7%) produced similar results. Additional analyses based on further co-variables was precluded because of incomplete primary study data. Overall, despite low DFU incidence post-DNS, clinical studies provide too low-level evidence and high-risk bias for any recommendations. Future randomized, control trials may validate DNS for DFU management.

Vulnerability extraction and prediction method based on improved information gain algorithm.

More and more attention has been paid to computer security, and its vulnerabilities urgently need more sensitive solutions. Due to the incomplete data of most vulnerability libraries, it is difficult to obtain pre-permission and post-permission of vulnerabilities, and construct vulnerability exploitation chains, so it cannot to respond to vulnerabilities in time. Therefore, a vulnerability extraction and prediction method based on improved information gain algorithm is proposed. Considering the accuracy and response speed of deep neural network, deep neural network is adopted as the basic framework. The Dropout method effectively reduces overfitting in the case of incomplete data, thus improving the ability to extract and predict vulnerabilities. These experiments confirmed that the excellent F1 and Recall of the improved method reached 0.972 and 0.968, respectively. Compared to the function fingerprints vulnerability detection method and K-nearest neighbor algorithm, the convergence is better. Its response time is 0.12 seconds, which is excellent. To ensure the reliability and validity of the proposed method in the face of missing data, the reliability and validity of Mask test are verified. The false negative rate was 0.3% and the false positive rate was 0.6%. The prediction accuracy of this method for existing permissions reached 97.9%, and it can adapt to the development of permissions more actively, so as to deal with practical challenges. In this way, companies can detect and discover vulnerabilities earlier. In security repair, this method can effectively improve the repair speed and reduce the response time. The prediction accuracy of post-existence permission reaches 96.8%, indicating that this method can significantly improve the speed and efficiency of vulnerability response, and strengthen the understanding and construction of vulnerability exploitation chain. The prediction of the posterior permission can reduce the attack surface of the vulnerability, thus reducing the risk of breach, speeding up the detection of the vulnerability, and ensuring the timely implementation of security measures. This model can be applied to public network security and application security scenarios in the field of computer security, as well as personal computer security and enterprise cloud server security. In addition, the model can also be used to analyze attack paths and security gaps after security accidents. However, the prediction of post-permissions is susceptible to dynamic environments and relies heavily on the updated guidance of security policy rules. This method can improve the accuracy of vulnerability extraction and prediction, quickly identify and respond to security vulnerabilities, shorten the window period of vulnerability exploitation, effectively reduce security risks, and improve the overall network security defense capability. Through the application of this model, the occurrence frequency of security vulnerability time is reduced effectively, and the repair time of vulnerability is shortened.

Estimation of the Average Yield of Cotton using Outlier Robust Geographically Weighted Regression Approach

The General Crop Estimation Survey (GCES) scheme requires a large number of Crop Cutting Experiments (CCEs) to be conducted to get a reliable estimate below the district level. However, conducting a large number of CCEs imposes a financial burden on Govt. agencies. Additionally, large-scale surveys like GCES often result in many outlier observations in the CCE data. To address this issue,this study was conducted to estimate the yield rate of cotton with a relatively fewer number of CCEs than the GCES scheme using the proposed Outlier Robust Geographically Weighted Regression (ORGWR) approach. Validation of the proposed methodology was done using the real CCE dataset of Amravati district for the 2012-13 agriculture year in Maharashtra. In this approach,the number of CCEs conducted for GCES scheme was reduced, and then this reduced number of the CCEs can be predicted using the proposed ORGWR approach. The predicted CCEs and the incomplete CCEs data are then combined to form a complete dataset. This complete dataset is used to calculate the crop yield accurately. The study conducted a comparison between the ORGWR approach and GCES methodology for estimating the average yield of cotton. The results showed that the ORGWR approach, when used with a lesser number of CCEs,yielded estimates that were almost equivalent to those obtained using the GCES methodology with the complete dataset. Moreover, the standard error of the estimate was reliable, indicating the validity of the results.

Clinical outcomes and coronary artery lesion characteristics of young patients with ST elevation myocardial infarction and no standard modifiable risk factors.

Among ST-elevation myocardial infarction (STEMI) patients, those with no standard modifiable risk factors (SMuRFs: hypertension, diabetes mellitus, hypercholesterolemia, and smoking) have higher 30-day mortality than those with SMuRFs. Differences in coronary lesion characteristics remain unclear. Data from STEMI patients aged ≤60 years from the Asia Pacific Evaluation of Cardiovascular Therapies Network (Australia, Hong Kong, Malaysia, Singapore, and Vietnam) was retrospectively analysed. Exclusion criteria included incomplete SMuRF data, prior myocardial infarction, or prior coronary revascularisation. Lesion type was defined using the American College of Cardiology criteria. Major adverse cardiovascular events (MACE) were defined as peri-procedural myocardial infarction, emergency coronary artery bypass surgery, cerebrovascular event, or mortality. Multiple logistic regressions were used. Of 4404 patients, 767 (17.4%) were SMuRFless. SMuRFless patients were more frequently younger (median age 51 vs. 53 years; p < 0.001), female (22.6% vs. 15.5%; p < 0.001), thrombolysed (20.1% vs. 12.5%; p < 0.001), and in cardiogenic shock (11.2% vs. 8.6%; p = 0.020). SMuRFless patients had significantly higher in-hospital MACE (7.2% vs. 4.3%; adjusted odds ratio [aOR] 2.25; 95% confidence interval [CI] 1.24-4.08; p = 0.008) but 1-year mortality was not significantly different (3.6% vs. 5.7%, aOR 0.58; 95% CI 0.06-6.12; p = 0.549). Compared with patients with SMuRFs (4918 lesions), the SMuRFless (940 lesions) had fewer type B2/C lesions (60.8% vs. 65.6%; p = 0.020) and fewer lesions ≥20 mm (51.1% vs. 57.1%; p = 0.002) but more procedural complications (5.1% vs. 2.7%; p < 0.001). Among young STEMI patients, the SMuRFless have shorter and less complex lesions, but worse procedural and short-term MACE outcomes.

Sustainable city tourism—A systematic analysis of Budapest and Mumbai

International Tourist arrivals, guest nights and their contribution to GDP are key indicators reflecting a country’s actual perception. A growing percentage of tourists prioritize environmental awareness across tourism products and services each year. Destinations aiming to meet the expectations of eco conscious travellers must center sustainability in their branding strategies. This approach aligns with UNWTO (World Tourism Organization of United Nations) Agenda 2030 of sustainable tourism development. This paper examines various dimensions of sustainability in tourism, focusing on Mumbai and Budapest. Using specific sustainability indicators, it employs sustainability city index to compare international tourism in these cities, which face distinct environmental and infrastructural challenges. By using specific sustainability indicators such as: (1) Carbon Emissions: Measurement of the total greenhouse gases produced by the city. (2) Proportion of Green Public Spaces: Evaluation of the percentage of urban areas dedicated to parks and natural spaces. (3) State of Infrastructure: Assessment of the quality and sustainability of urban infrastructure, including transportation systems. (4) Water Usage: Analysis of the amount of water consumed by the city and its conservation practices. (5) Waste Management: Review of the city’s effectiveness in managing and recycling waste. (6) Air Pollution: Monitoring of the levels of pollutants in the air to assess environmental health. This research provides a comprehensive view of how cities can attract environmentally conscious tourists. The findings offer guidance for policy makers and tourism professionals to align strategies with sustainable development goals. This detailed assessment highlights each city’s commitment to sustainability and delivers actionable insights for improving tourism strategies in accordance with global standards. While valuable for tourism professionals, it is important to note that this research covers only six SCI factors, with incomplete data for studied countries. The practical and social implications indicate areas needing improvement to enhance tourist appeal, beneficial for industry professionals and educational purposes. This comparative analysis aids in promoting sustainable tourism and can guide governments in achieving sustainability goals with raising awareness of environmental quality and conscious living.

FP4.13 - Intravenous morphine negatively affects length of stay following minimally invasive colorectal surgery

Abstract Aim Investigate the impact of intravenous (IV) morphine administered as patient controlled analgesia (PCA) following minimally invasive colorectal resections. Method Demographic, peri-operative and follow-up data from a prospectively maintained database were collected for all laparoscopic and robotic assisted colorectal procedures carried out at our centre. Results Our cohort included 38 colorectal resections completed with the DaVinci Xi (Intuitive Surgical, USA) robotic platform and 57 consecutive laparoscopic colorectal resections. 7 patients were excluded due to incomplete data. 51/88 (57.9%) of patients underwent a left sided resection and 24/88 (27.3%) had a stoma formed. We observed a median IV morphine dose of 15.5mg (IQR 15.5-43.5mg) and we sub-divided the cohort into those who received &amp;gt;15 mg of IV morphine and &amp;lt;15mg of morphine via PCA. Patients who received &amp;lt;15mg IV morphine were older (median age 75 Vs 67 years; p&amp;lt;0.001). There was no statistical difference in the body mass index, ASA grade, total operative time, or type of minimally invasive resection. The median length of stay was 4 days for the &amp;lt;15mg IV Morphine cases compared to 5 in the &amp;gt;15mg IV Morphine group (p&amp;lt;0.001). As expected, there was a quicker time to first flatus in the &amp;lt;15mg IV Morphine group (median 2 days Vs 3 days; p&amp;lt;0.001) and a shorter time to opening bowels (median 3 days Vs 4 days; p=0.003). There was no statistically significant difference in post-operative complications between the two groups. Conclusions Reducing postoperative IV Morphine dosing following minimally invasive colorectal resections may lead to prompter recovery.

Towards end-to-end structure determination from x-ray diffraction data using deep learning

Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form, by analyzing their x-ray diffraction (XRD) patterns. Since many materials are readily available as crystalline powder, powder crystallography is of growing usefulness to many fields. However, powder crystallography does not have an analytically known solution, and therefore the structural inference typically involves a laborious process of iterative design, structural refinement, and domain knowledge of skilled experts. A key obstacle to fully automating the inference process computationally has been formulating the problem in an end-to-end quantitative form that is suitable for machine learning, while capturing the ambiguities around molecule orientation, symmetries, and reconstruction resolution. Here we present an ML approach for structure determination from powder diffraction data. It works by estimating the electron density in a unit cell using a variational coordinate-based deep neural network. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity (as measured by structural similarity index) with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach does not presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and textured samples, paving the way to reconstruction of yet unresolved nanostructures.

Multivariate normality tests with two-step monotone missing data: a critical review with emphasis on the different methods of handling missing values

Two-step monotone missing data is a special case of missing data that attracted the attention of researchers since it not only appears frequently in applications but also since under the hypothesis of multivariate normality a unique and explicit solution to the likelihood equations exists. This importance prompted Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] to propose four procedures in total for testing multivariate normality with two-step monotone missing data. The aim of this paper is twofold. On the one, to compare, for the first time, the performance of the four aforementioned multivariate normality tests via a Monte Carlo study. The second aim of the paper is to compare the performance of the four existing testing procedures with the respective procedures derived as a combination of 12 popular methods for handling incomplete data and 6 multivariate normality tests for complete data. We show that in several cases the tests proposed by Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] are unable to achieve the nominal significance level. At the same time, no single procedure was found to be the most powerful in all situations of multivariate alternatives considered. As a consequence, the use of specific combinations of methods of handling missing data and testing multivariate normality with complete data is recommended.

Association between depression severity and trouble sleeping: A population-based study.

This study investigates the association between insomnia and depression severity, exploring sleep disturbances in individuals with depression. The aim is to establish a new foundation for managing patients with co-occurring depression and insomnia, using 2015 to 2016 National Health and Nutrition Examination Survey (NHANES) data. We employed a cross-sectional design, using NHANES data from 2015 to 2018. The study included 11,261 participants after excluding incomplete data. Depression severity, assessed using Patient Health Questionnaire-9 (PHQ-9) scores, served as the exposure variable. We considered various demographic and lifestyle factors as covariates in the multivariate adjustment model. Statistical analyses adhered to CDC recommendations, with sample weights incorporated to account for NHANES' complex sample design. Our study, encompassing 19,225 participants, revealed that higher PHQ-9 scores correlated with an increased likelihood of sleep disorders. In the fully adjusted model, a positive association emerged between PHQ-9 scores and trouble sleeping (OR = 3.95, 95% CI: 3.35-4.66, P < .0001). This relationship displayed an inverted U-shaped curve, with an inflection point at 28. Subgroup analysis and interaction tests indicated no reliance on factors such as gender, age, marital status, or BMI for the connection between depression severity and trouble sleeping (all P for interaction > .05). We identified a significant inverted U-shaped correlation between sleep disturbances and depression severity. This underscores the crucial importance of assessing sleep disorder risks in individuals with varying degrees of depression severity, facilitating personalized therapeutic interventions.

Extraction of complex pipeline features from incomplete point clouds

Abstract Control point detection in industrial pipelines, characterized by flanges&amp;#xD;and multiple passes, is critical for accurate virtual simulations and quality assessments&amp;#xD;in manufacturing. This paper introduces an innovative method for detecting control&amp;#xD;points in complex pipelines using incomplete point clouds, significantly streamlining&amp;#xD;the process. Our approach uniquely requires only straight sections and end-plane&amp;#xD;localizations as inputs, markedly reducing both data acquisition and processing times.&amp;#xD;We develop a robust feature descriptor to align the CAD model with incomplete&amp;#xD;point clouds, facilitating semantic automatic segmentation despite the lack of explicit&amp;#xD;semantic information. Following this, geometric primitives are fitted to the segmented&amp;#xD;clouds, and a cylindrical fitting algorithm tailored for incomplete data is introduced.&amp;#xD;The control points are computed based on the relative positions and geometric&amp;#xD;parameters of these primitives. Our method has been validated through experiments&amp;#xD;on several real-world industrial complex pipelines. The results confirm that our&amp;#xD;approach achieves a high measurement accuracy of 0.067 mm, even with point cloud&amp;#xD;incompleteness of up to 50%. These findings highlight the effectiveness of our method in&amp;#xD;accurately determining the geometric parameters of complex pipelines and suggesting its&amp;#xD;considerable potential for practical applications.&amp;#xD;