Laparoscopic colectomy for patients with poor American Society of Anesthesiology classifications.

INTRODUCTION: The American Society of Anesthesiologists (ASA) classification is used to assess the fitness of a patient for surgery. Whether laparoscopic surgery is appropriate for colorectal cancer patients with poor ASA performance status (PS) remains unclear. METHODS: Among 4585 patients who underwent colorectal surgery between 2016 and 2023, this study retrospectively reviewed all 458 patients with ASA-PS ≥3. Patients were divided into two groups: patients treated by open surgery (O group, n = 80); and patients treated by laparoscopic surgery (L group, n = 378). We investigated the impact of surgical approach on postoperative complications in patients with colorectal cancer and ASA-PS ≥3. RESULTS: Operation time was longer (170 min vs. 233 min, p < .001), blood loss was less (156 mL vs. 23 mL, p < .001), postoperative complications were less frequent (40.0% vs. 25.1%, p = .008), and hospital stay was shorter (23 days vs. 14 days, p < .001) in L group. Univariate analysis revealed rectal cancer, open surgery, longer operation time, and blood loss as factors significantly associated with postoperative complications. Multivariate analysis revealed open surgery (odds ratio [OR] 2.100, 95% confidence interval [CI] 1.164-3.788; p = .013) and longer operation time (OR 1.747, 95% CI 1.098-2.778; p = .018) as independent predictors of postoperative complications. CONCLUSION: Laparoscopic surgery provides favorable outcomes for colorectal cancer patients with poor ASA-PS.

Comparison and classification of photovoltaic system architectures for limiting the impact of the partial shading phenomenon.

This article proposes a comparison and classification of PV system architectures with the aim of limiting the impact of the partial shading phenomenon which remains one of the most harmful defects during the production of electrical energy with significant consequences on output power, current and voltage. the methodological approach used consists of analyzing in depth the main most recent architectures developed in the current literature with their advantages and disadvantages; then define five partial shading scenarios for different irradiance levels (1000W/m2; 900W/m2; 700W/m2; 500W/m2; 300W/m2), which will then be immediately applied to five other proposed architectures: SP (serial-parallel), BL (Bridge-Link); HC (Honey Comb); TCT (Total-Cross-Tied); TSPL (Triple Series Parallel Ladder); the values obtained at the output with each of its architectures will be used for an in-depth descriptive analysis with PCA (principal component analysis) in statistics but to carry out a comparative analysis between the architectures. All with a Matlab Simulink 2022.b software environment. The results obtained offer a strong positive correlation for TCP and TSPL architectures with better weight compared to other architectures. All in accordance with the IEEE-519-2022 standard. This work is therefore positioned as a contribution to the optimization of the performance of electrical energy production through the use of PV systems which today represent widely used alternatives in the renewable energy register.

Pediatric spinal alignment and spinal development.

Background: Knowledge of the growth spurt and remaining growth is essential for managing musculoskeletal diseases in children. Accurate prediction of curve progression and timely interventions are crucial, particularly for conditions like adolescent idiopathic scoliosis (AIS). Methods: This study conducted a comprehensive review and synthesis of existing literature on spinal growth, skeletal maturity classifications, and the evolution of sagittal alignment parameters during childhood and adolescence. Key anatomical elements involved in spinal development, natural history of spinal growth, and skeletal maturity assessment systems were analyzed. Results: The analysis highlighted that key parameters such as Pelvic incidence (PI), Pelvic tilt (PT), and Lumbar lordosis (LL) increase significantly with growth, especially during the pubertal growth spurt. In contrast, Sacral slope (SS) remains relatively constant, and Thoracic kyphosis (TK) shows a slight increase. Additionally, there is a posterior shift in the center of gravity as children grow, reflecting progressive postural maturation. The study also reviewed and compared various maturity classification systems, noting the reliability and clinical implications of systems like the Sanders Maturity Stage (SMS) and Tanner-Whitehouse III. Conclusions: Reliable maturity classification systems, such as the Sanders Maturity Stage (SMS) and Tanner-Whitehouse III, allow for tailored treatments to individual growth patterns. Integrating these classification systems into clinical practice enables precise prediction of curve progression and timely therapeutic interventions. This includes options from bracing to surgical techniques like growing rods or vertebral body tethering (VBT), with growth modulation being a key factor in achieving successful outcomes.

CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

Validation of classification system for isolated superior mesenteric artery dissections using image-based computational flow analysis.

BACKGROUND AND OBJECTIVE: The isolated superior mesenteric artery dissection (ISMAD) is a rare but potentially fatal vascular disorder. Classifications for ISMAD were previously proposed based on morphometric features. However, the classification systems were not standardized and verified yet. This study conducted computational flow analysis to validate the latest classification system of ISMAD and aid clinical decision-making based on hemodynamic parameters. METHODS: 62 patients with ISMAD were included and classified into different types according to false lumen structures (five types, Type I-V) and true lumen patency (two types, Type P and Type S) according to Qiu classification system. Computational fluid dynamics and three-dimensional structural analyses were conducted on the basis of computed tomography angiography datasets. Quantitative and qualitative functional analyses were performed via parameters of interest including volume flow of each minute, pressure drop, pressure gradient, the derivative parameters of wall shear stress such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and the relative residence time (RRT). Statistical analyses were conducted among different ISMAD types. RESULTS: TAWSS, OSI and RRT showed significant difference among different types when classified using false lumen structures. In detail, Type IV showed significantly higher TAWSS than other types (p = 0.007). OSI was obviously higher in Type II (p = 0.015). Type IV also presented the lowest RRT (p = 0.005). The pressure drop, pressure gradient, OSI and RRT showed higher value in Type S than that in Type P, demonstrating a statistical significance with p values of 0.017, 0.041, 0.001 and 0.012, respectively. While Type P had larger volume flow than Type S (p = 0.041). CONCLUSIONS: The notable differences in hemodynamic features among different types demonstrated the feasibility of Qiu classification system. The evaluation based on hemodynamic simulation might also provide insights into risk identification and guide therapeutic decisions for ISMAD.

Imperfect symmetry facilitated the evolution of specificity and high-order stoichiometry in vertebrate hemoglobin.

Many proteins form paralogous multimers - molecular complexes in which evolutionarily related proteins are arranged into specific quaternary structures. Little is known about the mechanisms by which they acquired their stoichiometry (the number of total subunits in the complex) and heterospecificity (the preference of subunits for their paralogs rather than other copies of the same protein). Here we use ancestral protein reconstruction and biochemical experiments to study historical increases in stoichiometry and specificity during the evolution of vertebrate hemoglobin (Hb), a α2ß2 heterotetramer that evolved from a homodimeric ancestor after a gene duplication. We show that the mechanisms for this evolutionary transition was simple. One hydrophobic substitution in subunit ß after the gene duplication was sufficient to cause the ancestral dimer to homotetramerize with high affinity across a new interface. During this same interval, a single-residue deletion in subunit α at the older interface conferred specificity for the heterotetrameric form and the trans-orientation of subunits within it. These sudden transitions in stoichiometry and specificity were possible because the interfaces in Hb are isologous - involving the same surface patch on interacting subunits, rotated 180° relative to each other - but the symmetry is slightly imperfect. This architecture amplifies the impacts of individual mutations on stoichiometry and specificity, especially in higher-order complexes, and allows single substitutions to differentially affect heteromeric vs homomeric interactions. Many multimers are isologous, and symmetry in proteins is always imperfect; our findings therefore suggest that elaborate and specific molecular complexes may often evolve via simple genetic and physical mechanisms.

Imaging of glenohumeral osteoarthritis: Reliability and reproducibility of radiological classifications.

BACKGROUND: Glenohumeral degenerative joint disease may affect up to 20% of the population. There are several classification systems of this disease in the scientific literature. OBJECTIVE: The aim of this study is to determine the reliability and reproducibility of glenohumeral osteoarthritis classification systems. METHODS: We assessed glenohumeral plain radiographs performed in a University Hospital. These radiographs were graded into nine radiological classification systems by two observers on two evaluations. Patients who have performed CT/MRI scan were staged according to the Walch classification. The intra-observer and inter-observer reliability of the classification schemes were determined by using Cohen's weighted kappa coefficient. RESULTS: A total of 752 patients were included in the study (52.4% females and 47.6% males), mean aged 66.2 ± 16.3 years. The intra-observer and inter-observer reliabilities were 0.543 (observer 1), 0.630 (observer 2), and 0.760 (inter-observer) for the Weinstein grading system; 0.661, 0.706, and 0.761 for the Guyette grading system; 0.575, 0.679 and 0.704 for the Kellgren and Lawrence classification; 0.817, 0.816 e 0.871 for the Samilson and Prieto classification; 0.791, 0.811 and 0.847 for the Allain modification; 0.797, 0.842 and 0.860 for the Gerber modification; 0.773, 0.827 and 0.828 for the Buscayret modification; 0.584, 0.648 and 0.755 for the Hawkins and Angelo classification; 0.661, 0.749 and 0.764 for the Rosenberg classification. Intra-observer reliability for MRI was 0.757 (observer 1) and 0.675 (observer 2), while intra-observer reliability for CT was 0.811 (observer 1) and 0.653 (observer 2). Inter-observer reliabilities were 0.790 for MRI and 0.673 for CT. CONCLUSION: The classification systems according to Weinstein, Guyette, Hawkins and Angelo, Rosenberg and the modifications of the Samilson and Prieto classification according to Allain, Gerber and Buscayret showed a comparable reliability with the commonly used glenohumeral osteoarthritis grading systems, Samilson and Prieto and Kellgren and Lawrence. Thus, they are recommended for clinical and especially scientific purposes.

Natural hypoglycaemic bioactives: Newer avenues and newer possibilities.

The incidences of endocrine and metabolic disorders like diabetes have increased worldwide. Several proposed molecular pathways mechanisms for the management of diabetes have been identified, but glycaemic control is still a challenging task in the drug discovery process. Most of the drug discovery processes lead to numerous scaffolds that are prominent in natural products. The review deals with the natural bioactives as an α-amylase inhibitors, α-glucosidase inhibitors, protein tyrosine phosphatase-1B inhibitors, dipeptidyl peptidase-IV inhibitors, G-protein coupled receptors-40 agonists, PPAR-γ agonists and the activators of 5'-adenosine monophosphate-activated protein kinase and glucokinase. So, in this review, we focused on the hypoglycaemic bioactives, which will assist scientific developers, traditional medicinal practitioners, and readers to discover some potent antidiabetic molecules. Strategies like chemometric approaches, scaffold hopping, and total synthesis of natural products by group modification or ring opening/closing mechanism could be useful for the development of novel hit/lead antidiabetic molecules. The study concludes that each phyto molecule inherits a potential to get explored by repurposing techniques for various antidiabetic targets and offer an alternative antidiabetic therapeutic medicinal potential.

Tropical Logistic Regression Model on Space of Phylogenetic Trees.

Classification of gene trees is an important task both in the analysis of multi-locus phylogenetic data, and assessment of the convergence of Markov Chain Monte Carlo (MCMC) analyses used in Bayesian phylogenetic tree reconstruction. The logistic regression model is one of the most popular classification models in statistical learning, thanks to its computational speed and interpretability. However, it is not appropriate to directly apply the standard logistic regression model to a set of phylogenetic trees, as the space of phylogenetic trees is non-Euclidean and thus contradicts the standard assumptions on covariates. It is well-known in tropical geometry and phylogenetics that the space of phylogenetic trees is a tropical linear space in terms of the max-plus algebra. Therefore, in this paper, we propose an analogue approach of the logistic regression model in the setting of tropical geometry. Our proposed method outperforms classical logistic regression in terms of Area under the ROC Curve in numerical examples, including with data generated by the multi-species coalescent model. Theoretical properties such as statistical consistency have been proved and generalization error rates have been derived. Finally, our classification algorithm is proposed as an MCMC convergence criterion for Mr Bayes. Unlike the convergence metric used by Mr Bayes which is only dependent on tree topologies, our method is sensitive to branch lengths and therefore provides a more robust metric for convergence. In a test case, it is illustrated that the tropical logistic regression can differentiate between two independently run MCMC chains, even when the standard metric cannot.

A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture.

Over the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be employed for malicious activities with public safety threats. However, detecting drones instantly and efficiently is a very difficult task due to their tiny size and swift flights. This paper presents a novel drone detection method using deep convolutional learning and deep transfer learning. The proposed algorithm employs a new feature extraction network, which is added to the modified YOU ONLY LOOK ONCE version2 (YOLOv2) network. The feature extraction model uses bypass connections to learn features from the training sets and solves the "vanishing gradient" problem caused by the increasing depth of the network. The structure of YOLOv2 is modified by replacing the rectified linear unit (relu) with a leaky-relu activation function and adding an extra convolutional layer with a stride of 2 to improve the small object detection accuracy. Using leaky-relu solves the "dying relu" problem. The additional convolution layer with a stride of 2 reduces the spatial dimensions of the feature maps and helps the network to focus on larger contextual information while still preserving the ability to detect small objects. The model is trained with a custom dataset that contains various types of drones, airplanes, birds, and helicopters under various weather conditions. The proposed model demonstrates a notable performance, achieving an accuracy of 77% on the test images with only 5 million learnable parameters in contrast to the Darknet53 + YOLOv3 model, which exhibits a 54% accuracy on the same test set despite employing 62 million learnable parameters.

Roussouly classification of adult spinal deformity.

With an estimated prevalence of 68% among healthy adults without a previous diagnosis of scoliosis, adult spinal deformities are a growing concern as the population ages. Our understanding of this growing concern has been historically guided by previous studies performed on the pediatric population. Over time, different classifications have been developed with their own respective limitations. The Roussouly classification was the first classification to describe the shapes of an asymptomatic spine. It considers lumbar lordosis, pelvic incidence, and the inflection point from lumbar lordosis to thoracic kyphosis to attempt to stratify the shapes of an asymptomatic spine. This classification aims to guide treatment, provide information regarding prognosis, allow stratification for research, and be highly reproducible. Overall, the Roussouly classification is a novel way to think about sagittal malalignment, considering the patient's individual anatomy, while allowing for communication between surgeons. Additionally, it has proven to be a reliable system that provides prognostic value for clinicians and may minimize complications when a patient's sagittal alignment is optimized using this classification system.

Emerging Trends in Hybrid Nanoparticles: Revolutionary Advances and Promising Biomedical Applications.

Modern nanostructures must fulfill a wide range of functions to be valuable, leading to the combination of various nano-objects into hierarchical assemblies. Hybrid Nanoparticles (HNPs), comprised of multiple types of nanoparticles, are emerging as nanoscale structures with versatile applications. HNPs offer enhanced medical benefits compared to basic combinations of distinct components. They address the limitations of traditional nanoparticle delivery systems, such as poor water solubility, nonspecific targeting, and suboptimal therapeutic outcomes. HNPs also facilitate the transition from anatomical to molecular imaging in lung cancer diagnosis, ensuring precision. In clinical settings, the selection of nanoplatforms with superior reproducibility, cost-effectiveness, easy preparation, and advanced functional and structural characteristics is paramount. This study aims toextensively examine hybrid nanoparticles, focusing on their classification, drug delivery mechanisms, properties of hybrid inorganic nanoparticles, advancements in hybrid nanoparticle technology, and their biomedical applications, particularly emphasizing the utilization of smart hybrid nanoparticles. PHNPs enable the delivery of numerous anticancer, anti-leishmanial, and antifungal drugs, enhancing cellular absorption, bioavailability, and targeted drug delivery while reducing toxic side effects.

Development of a crosswalk to convert French PCS2003 into international ISCO88 occupational classifications. Application to the Occupational Asthma-specific Job-Exposure Matrix (OAsJEM).

INTRODUCTION: The problem of transcoding is recurrent when researchers wish to link occupational data from cohorts to Job-Exposure Matrices (JEMs) which were not set up in the same classifications. The Occupational Asthma-specific JEM (OAsJEM) is a JEM developed for assessing exposure to agents known at risk for asthma for jobs coded with ISCO88 occupation classification. To apply the OAsJEM in the CONSTANCES cohort, in which jobs and industries were coded with French PCS2003 and NAF2008 classifications respectively, we developed a crosswalk to convert jobs from PCS2003 into ISCO88 classification. METHODS: This work was carried out by 2 skilled coders and 1 novice coder who have worked independently by using different tools and transcoding strategies defined a priori. Consensus meetings were organized with skilled coders to define the final crosswalk. This work was elaborated in 2 steps: (i) for 38 ISCO88 codes classified as potentially exposed to cleaning and disinfection products by the OAsJEM, and (ii) for all jobs from the ISCO88 classification. A comparison between the 3 initial coder's crosswalk proposals and the final crosswalk was made for the 38 ISCO codes in step (i). RESULTS: The final crosswalk provided 998 matches between the 482 4-digit PCS2003 codes, 308 4-digit ISCO88 codes, and 31 3-digit ISCO88 codes. Information regarding the NAF2008 industry classification was also used in some cases to improve the final crosswalk. For the selected 38 ISCO88 codes, the final crosswalk provided 110 combinations, but the number of proposed ISCO88-PCS2003 couples by each of the 3 coders varied greatly from 68 to 153. In addition, an important variability between the 3 coders were observed among the number of common combinations between the initial coder's proposals and the final crosswalk (from 47% to 78%). DISCUSSION: We have developed a crosswalk specifically for an application of the OAsJEM in population-based surveys using the PCS2003 occupation classification. The development of this crosswalk is of great interest for the use of OAsJEM on the data of the CONSTANCES cohort and on any other survey with occupational data coded according to the French classifications. This OAsJEM crosswalk could be used by other teams to evaluate occupational exposures known to be at risk of asthma from occupational calendars coded with French classifications.

Concordance Analysis of Lower Third Molar Surgery Classifications: A Comparative Study.

The high frequency and complexity of mandibular third molar (M3M) surgery have led several authors to the development of classification systems for better evaluation and management in oral surgery. This study compared the classifications of Juodzabalys and Daugela et al. (JD), Sammartino et al., Chang et al., Jhamb et al., Maglione et al., and Nemsi et al. to understand the concordance between the scores of M3M surgery. Two types of analysis were conducted: the relationship between the M3M and the inferior alveolar nerve (IAN), and the overall difficulty score based on the tooth's angulation and its spatial position with the adjacent structure. The analysis of the classifications on the relationship between M3M and IAN resulted in a concordance of 26.1%. In the pairwise comparisons, the classifications of Nemsi et al. and Jhamb et al. showed the highest concordance of 59.5%. Analyzing the total scores difficulty, the JD et al., Chang et al., and Sammartino et al. classifications demonstrated a concordance level of 25.5%. A pairwise assessment revealed a higher concordance degree between the classifications of Sammartino et al. and Chang et al. (57.4%). The results highlight the limits in establishing a comprehensive and objective classification for the surgical difficulty of M3M, possibly attributed to variations in the methodology for computing total scores. An objective, automated, and non-operator-dependent classification method for assessing the surgical difficulty of M3M is still needed.

Evaluation of European LeukemiaNet 2022 risk classification in patients undergoing allogeneic haematopoietic stem cell transplantation for acute myeloid leukaemia: Identification of a very poor prognosis genetic group.

European LeukemiaNet refined their risk classification of acute myeloid leukaemia (AML) in 2022 (ELN 2022) according to the two new myeloid classifications published the same year. We have retrospectively assessed the prognostic value of the ELN 2022 in 120 AML patients undergoing allogeneic haematopoietic cell transplantation (allo-HCT), including 99 in first complete response (CR1) from 2011 to 2021 in our centre. Adverse risk patients (Adv) presented inferior outcome in terms of overall survival (OS) and leukaemia-free survival (LFS) (OS [p = 0.003], LFS [p = 0.02]), confirmed in multivariate analysis (hazard ratio [HR] for OS = 2.00, p = 0.037). These results were also seen in patients allografted in CR1. Further analysis identified a subgroup named adverse-plus (AdvP), including complex karyotype, MECOM(EVI1) rearrangements and TP53 mutations, with worse outcomes than the rest of groups of patients, including the Adv (HR for OS: 3.14, p < 0.001, HR for LFS: 3.36, p < 0.001), with higher 2-year cumulative incidence of relapse (p < 0.001). Notably, within this analysis, the outcome of Adv and intermediate patients were similar. These findings highlight the prognostic value of ELN 2022 in patients undergoing allo-HCT, which can be improved by the recognition of a poor genetic subset (AdvP) within the Adv risk group.

In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data.

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

Meningioma: current updates on genetics, classification, and mouse modeling.

Meningiomas, the most common primary brain tumors in adults, are often benign and curable by surgical resection. However, a subset is of higher grade, shows aggressive growth behavior as well as brain invasion, and often recurs even after several rounds of surgery. Increasing evidence suggests that tumor classification and grading primarily based on histopathology do not always accurately predict tumor aggressiveness and recurrence behavior. The underlying biology of aggressive treatment-resistant meningiomas and the impact of specific genetic aberrations present in these high-grade tumors is still only insufficiently understood. Therefore, an in-depth research into the biology of this tumor type is warranted. More recent studies based on large-scale molecular data such as whole exome/genome sequencing, DNA methylation sequencing, and RNA sequencing have provided new insights into the biology of meningiomas and have revealed new risk factors and prognostic subtypes. The most common genetic aberration in meningiomas is functional loss of NF2 and occurs in both low- and high-grade meningiomas, whereas NF2-wildtype meningiomas are enriched for recurrent mutations in TRAF7, KLF4, AKT1, PI3KCA, and SMO and are more frequently benign. Most meningioma mouse models are based on patient-derived xenografts and only recently have new genetically engineered mouse models of meningioma been developed that will aid in the systematic evaluation of specific mutations found in meningioma and their impact on tumor behavior. In this article, we review recent advances in the understanding of meningioma biology and classification and highlight the most common genetic mutations, as well as discuss new genetically engineered mouse models of meningioma.

Meniscal ramp lesion classification systems: A systematic review.

PURPOSE: To describe the proposed classification systems for meniscal ramp lesions (RLs) in the literature and evaluate their accuracy and reliability. METHODS: A systematic search was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-analyses guidelines utilising PubMed, Embase and Cochrane Library databases. Level I-IV studies referencing RLs along with either an arthroscopic- or magnetic resonance imaging (MRI)-based classification system used to describe RL subtypes were included. RESULTS: In total, 21 clinical studies were included. Twenty-seven (79%) of the included studies were published in 2020 or later. There were four main classification systems proposed within the literature (two arthroscopic-, two MRI-based), describing tear patterns, mediolateral extent, associated ligament disruption and stability of the lesion. The first classification was proposed in 2015 by Thaunat et al. and is referenced in 22 (64.7%) of the included studies. The application of the Thaunat et al. criteria to MRI showed variable sensitivity (31.70%-93.8%) and interobserver agreement (k = 0.55-0.80). The Greif et al. modification to the Thaunat et al. system was referenced in 32.4% of the included studies and had a substantial interobserver agreement (k = 0.8). Stability to probing and specific tear location were each used to classify RLs in 28.6% and 23.8% of the included clinical studies, respectively. CONCLUSION: Although there has been a recent increase in the recognition and treatment of meniscal RLs, there is limited consistency in descriptive classifications used for this pathology. Current RL classification systems based on preoperative MRI have variable reliability, and arthroscopic examination remains the gold standard for diagnosis and classification. LEVEL OF EVIDENCE: Level IV.

Education Experiments in Latin America: Empirical Evidence to Guide Evaluation Design.

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

Retroperitoneal mixed neuroendocrine non-neuroendocrine neoplasm: A case report and review of literature.

The existence of both neuroendocrine and non-neuroendocrine histology in variable proportion in a lesion has been described by the World Health Organisation (WHO) as mixed neuroendocrine and non-neuroendocrine neoplasm (MiNEN). The pathogenesis of this tumour remains controversial but molecular studies point towards a common monoclonal origin. Tumours are classified as functioning and nonfunctioning based on substances secreted. The nonfunctioning tumours may be discovered due to its local effect. Presented is a 66-year-old male with an intra-abdominal mass, underwent laparotomy and excision biopsy with transient right lower limb lymphoedema. Histology confirmed retroperitoneal MiNEN with no evidence of tumour recurrence 12 months following surgery. MiNENs should be considered as a differential diagnosis in patients with intra-abdominal mass. Surgical resection is recommended as this may offer the best treatment option.