Reliability of thoracolumbar burst fracture classification in the Swedish Fracture Register.

BACKGROUND: The Swedish Fracture Register (SFR) is a national quality register for all types of fractures in Sweden. Spine fractures have been included since 2015 and are classified using a modified AOSpine classification. The aim of this study was to determine the accuracy of the classification of thoracolumbar burst fractures in the SFR. METHODS: Assessments of medical images were conducted in 277 consecutive patients with a thoracolumbar burst fracture (T10-L3) identified in the SFR. Two independent reviewers classified the fractures according to the AOSpine classification, with a third reviewer resolving disagreement. The combined results of the reviewers were considered the gold standard. The intra- and inter-rater reliability of the reviewers was determined with Cohen's kappa and percent agreement. The SFR classification was compared with the gold standard using positive predictive values (PPV), Cohen's kappa and percent agreement. RESULTS: The reliability between reviewers was  high (Cohen's kappa 0.70-0.97). The PPV for correctly classifying burst fractures in the SFR was high irrespective of physician experience (76-89%), treatment (82% non-operative, 95% operative) and hospital type (83% county, 95% university). The inter-rater reliability of B-type injuries and the overall SFR classification compared with the gold standard was low (Cohen's kappa 0.16 and 0.17 respectively). CONCLUSIONS: The SFR demonstrates a high PPV for accurately classifying burst fractures, regardless of physician experience, treatment and hospital type. However, the reliability of B-type injuries and overall classification in the SFR was found to be low. Future studies on burst fractures using SFR data where classification is important should include a review of medical images to verify the diagnosis.

Diverticulitis in the Under-40 Population.

BACKGROUND: As obesity and lifestyle factors become more prevalent in younger populations, we are diagnosing and treating diverticulitis in younger patients. In this study, the demographics, risk factors for the development, and treatment of acute diverticulitis were assessed focusing on patients under the age of 40. METHODS: A retrospective review of the electronic medical records of a cohort of subjects diagnosed with diverticulitis was performed. Inclusion criteria included patients aged 18-40 who were treated for acute diverticulitis with or without any complications. RESULTS: Of the 109 patients, 40 patients required surgery, and 69 patients were managed conservatively. Analysis showed that the Hinchey classification (p<0.001) was the strongest predictor of treatment modality. CONCLUSIONS: As the incidence of diverticulitis has increased in recent decades, so too has the frequency with which elective surgical procedures are performed as treatment. While these procedures are vital components in the management of diverticulitis, the majority of research comparing conservative versus surgical treatments has been done in patients over 50 years old. Although diverticulitis has been classically thought of as a disease of the elderly, it has become more prevalent in younger populations due to the rise of obesity and lifestyle modification in the under-40 population. Although the prevalence of treatment and diagnosis of acute diverticulitis in younger patients has risen, there is a paucity of data surrounding treatment protocols for diverticulitis in association with patient symptoms for patients under the age of 40 years old. Our study has found that there is a higher incidence of complications in diverticulitis in patients under the age of 40. Additionally, when considering the pattern of complication presentation in younger patients with complicated diverticulitis, surgical intervention might not be appropriate. The current treatment algorithm relates diverticulitis complications with surgical interventions. However, our data suggest that patients under the age of 40 presenting with abscesses or strictures may not need surgical intervention. This information could be particularly helpful in guiding physicians and younger patients in selecting the best choice of care and minimizing complications. Additionally, further research should help guide treatment protocol in this specific population of patients, as there is a lack of established guidelines pertaining to diverticulitis surrounding younger patients.

Types of Epiglottic Collapse in Breathing Sleep Disorder and Their Impact in Clinical Practice.

Introduction Obstructive sleep apnea (OSA) is a severe form of sleep-disordered breathing (SDB) that is strongly correlated with comorbidities, in which epiglottic collapse (EC) and other contributing factors are involved. Objectives To evaluate the occurrence of EC in OSA patients through drug-induced sleep endoscopy (DISE) and to determine the factors contributing to EC. Methods A retrospective study of 37 adult patients using medical history. Patients were assessed for laryngopharyngeal reflux (LPR) and lingual tonsil hypertrophy (LTH) using reflux symptom index and reflux finding score (RFS); for OSA using polysomnography, and for airway collapse through DISE. An independent t -test was performed to evaluate risk factors, including the involvement of three other airway structures. Results Most EC patients exhibited trap door epiglottic collapse (TDEC) (56.8%) or pushed epiglottic collapse (PEC) (29.7%). Lingual tonsil hypertrophy, RFS, and respiratory effort-related arousal (RERA) were associated with epiglottic subtypes. Laryngopharyngeal reflux patients confirmed by RFS (t(25) = -1.32, p = 0.197) tended to suffer PEC; LTH was significantly associated (X2(1) = 2.5, p = 0.012) with PEC (odds ratio [OR] value = 44) in grades II and III LTH patients; 11 of 16 TDEC patients had grade I LTH. Pushed epiglottic collapse was more prevalent among multilevel airway obstruction patients. A single additional collapse site was found only in TDEC patients. Conclusion Laryngopharyngeal reflux causes repetitive acid stress toward lingual tonsils causing LTH, resulting in PEC with grade II or III LTH. Trap door epiglottic collapse requires one additional structural collapse, while at least two additional collapse sites were necessary to develop PEC. Respiratory effort-related arousal values may indicate EC.

Convalescent plasma transfusion for immunocompromised viremic patients with COVID-19: A retrospective multicenter study.

This study aims to assess the safety, virological, and clinical outcomes of convalescent plasma transfusion (CPT) in immunocompromised patients hospitalized for coronavirus disease 2019 (COVID-19). We conducted a retrospective multicenter cohort study that included all immunosuppressed patients with COVID-19 and RNAemia from May 2020 to March 2023 treated with CPT. We included 81 patients with hematological malignancies (HM), transplants, or autoimmune diseases (69% treated with anti-CD20). Sixty patients (74%) were vaccinated, and 14 had pre-CPT serology >264 BAU/mL. The median delay between symptom onset and CPT was 23 days [13-31]. At D7 post-CPT, plasma PCR was negative in 43/64 patients (67.2%), and serology became positive in 25/30 patients (82%). Post-CPT positive serology was associated with RNAemia negativity (p < 0.001). The overall mortality rate at D28 was 26%, being higher in patients with non-B-cell HM (62%) than with B-cell HM (25%) or with no HM (11%) (p = 0.02). Patients receiving anti-CD20 without chemotherapy had the lowest mortality rate (8%). Positive RNAemia at D7 was associated with mortality at D28 in univariate analysis (HR: 3.05 [1.14-8.19]). Eight patients had adverse events, two of which were severe but transient. Our findings suggest that CPT can abolish RNAemia and ameliorate the clinical course in immunocompromised patients with COVID-19.

A new classification for surgical NEC during exploratory laparotomy: introduction and reproducibility assessment.

PURPOSE: Variability in necrosis patterns and operative techniques in surgical necrotizing enterocolitis (NEC) necessitates a standardized classification system for consistent assessment and comparison. This study introduces a novel intraoperative reporting system for surgical NEC, focusing on reliability and reproducibility. METHODS: Analyzing surgical NEC cases from January 2018 to June 2023 at two tertiary neonatal and pediatric surgery units, a new classification system incorporating anatomical details and intestinal involvement extent was developed. Its reproducibility was quantified using kappa coefficients (κ) for interobserver and intraobserver reliability, assessed by four specialists. Furthermore, following surgery, the occurrence of mortality and enteric autonomy were evaluated on the basis of surgical decision-making of the novel intraoperative classification system for surgical NEC. RESULTS: In total, 95 patients with surgical NEC were included in this analysis. The mean κ value of the intra-observer reliability was 0.889 (range, 0.790-0.941) for the new classification, indicating excellent agreement and the inter-observer reliability was 0.806 (range, 0.718-0.883), indicating substantial agreement. CONCLUSION: The introduced classification system for surgical NEC shows high reliability, deepening the understanding of NEC's intraoperative exploration aspects. It promises to indicate operative strategies, enhance prognosis prediction, and substantially facilitate scholarly communication in pediatric surgery. Importantly, it explores the potential for a standardized report and may represent a step forward in classifying surgical NEC, if pediatric surgeons are open to change.

[Current WHO classification (2022) of neuroendocrine neoplasms]. / Aktuelle WHO-Klassifikation (2022) neuroendokriner Neoplasien.

CLINICAL ISSUE: After the first description of the "carcinoid tumors" by the pathologist Siegfried Oberndorfer in Munich, the classification system of neuroendocrine neoplasms (NENs) is still a challenge and an evolving concept. METHODICAL INNOVATIONS: The new WHO classification system proposed a framework for universal classification. ACHIEVEMENTS: The new WHO classification system recognizes two distinct families distinguished by genetic, morphology and clinical behaviour: Well differentiated NENs are defined as neuroendocrine tumor (NET G1, G2, G3), while poorly differentiated ones are defined as neuroendocrine carcinoma (NEC, G3) and further subdivided into small and large cell carcinoma. All NENs are characterized by the expression of synaptophysin and chromogranin A, Ki-67 and morphology. MOLECULAR PATHOLOGY: The morphological NEN dichotomy is supported by genetic alterations. NECs show TP53 and RB1 alterations that are absent in NETs and are therefore useful for differentiating between NETs and NECs. PRACTICAL RECOMMENDATIONS: All NENs are divided into well-differentiated neuroendocrine tumor (NET G1, G2, G3) or poorly differentiated neuroendocrine carcinoma (NEC, G3). They are categorized by morphology, mitotic count and immunohistochemistry with synaptophysin, chromogranin and Ki-67.

Study on Fine-Grained Visual Classification of Low-Resolution Urinary Erythrocyte.

The morphological analysis test item of urine red blood cells is referred to as "extracorporeal renal biopsy," which holds significant importance for medical department testing. However, the accuracy of existing urine red blood cell morphology analyzers is suboptimal, and they are not widely utilized in medical examinations. Challenges include low image spatial resolution, blurred distinguishing features between cells, difficulty in fine-grained feature extraction, and insufficient data volume. This article aims to improve the classification accuracy of low-resolution urine red blood cells. This paper proposes a super-resolution method based on category-aware loss and an RBC-MIX data enhancement approach. It optimizes the cross-entropy loss to maximize the classification boundary and improve intra-class tightness and inter-class difference, achieving fine-grained classification of low-resolution urine red blood cells. Experimental outcomes demonstrate that with this method, an accuracy rate of 97.8% can be achieved for low-resolution urine red blood cell images. This algorithm attains outstanding classification performance for low-resolution urine red blood cells with only category labels required. This method can serve as a practical reference for urine red blood cell morphology examination items.

From PDB files to protein features: a comparative analysis of PDB bind and STCRDAB datasets.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

The FDA Reclassification of Cervical Pedicle and Lateral Mass Screws: A Case Study in Regulatory History.

The classification of medical devices by the Food and Drug Administration (FDA) involves rigorous scrutiny from specialized panels that designate devices as Class I, II, or III depending on their levels of relative risk to patient health. Posterior rigid pedicle screw systems were first classified by the FDA in 1984 and have since revolutionized the treatment of many spine pathologies. Despite this early classification by the FDA, posterior cervical pedicle and lateral mass screws were not reclassified from unclassified to Class III and then to Class II until 2019, nearly 35 years after their initial classification. This reclassification process involved a decades-long interplay between the FDA, formal panels, manufacturers, academic leaders, practicing physicians, and patients. It was delayed by lawsuits and a paucity of data demonstrating the ability to improve outcomes for cervical spinal pathologies. The off-label use of thoracolumbar pedicle screw rigid fixation systems by early adopters assisted manufacturers and professional organizations in providing the necessary data for the reclassification process. This case study highlights the collaboration between physicians and professional organizations in facilitating FDA reclassification and underscores changes to the current classification process that could avoid the prolonged dichotomy between common medical practice and FDA guidelines.

Gender-responsive Classification of Women in Prison: A Typology Based on Mental Health Symptoms and Coping Strategies.

Despite the increase in the incarceration rates of women, most correctional practices are still normed on male samples, including prison classification. Moreover, those classifications do not take into account women's particular experiences, needs, and unique pathways to criminality. The current research proposes a typology based on female prisoners' mental health symptoms and coping strategies. The data was derived from a survey conducted with 194 women housed in a Northeastern prison. A two-step clustering analysis was used to obtain three classification types-each with different symptomatology, coping mechanisms, demographic, and background characteristics. The results suggest that identifying and relying on needs-based typologies has important correctional policy implications in terms of the management and the treatment of incarcerated women.

A pixel-wise labelled dataset of Moroccan aircraft emergency landing sites for semantic segmentation applications.

Detecting emergency aircraft landing sites is crucial for ensuring passenger and crew safety during unexpected forced landings caused by factors like engine malfunctions, adverse weather, or other aviation emergencies. In this article, we present a dataset consisting of Google Maps images with their corresponding masks, specifically crafted with manual annotations of emergency aircraft landing sites, distinguishing between safe areas with suitable conditions for emergency landings and unsafe areas presenting hazardous conditions. Drawing on detailed guidelines from the Federal Aviation Administration, the annotations focus on key features such as slope, surface type, and obstacle presence, with the goal of pinpointing appropriate landing areas. The proposed dataset has 4180 images, with 2090 raw images accompanied by their corresponding annotation instances. This dataset employs a semantic segmentation approach, categorizing the image pixels into two "Safe" and "Unsafe" classes based on authenticated terrain-specific attributes, thereby offering a nuanced understanding of the viability of various landing sites in emergency scenarios.

Source analysis of chlorine in municipal solid waste under waste classification: a case study of Hangzhou, China.

Inorganic chlorine is susceptible to water and soil salinization due to its non-degradability and high mobility. To clarify the environmental risks associated with the active inorganic chlorine in municipal solid waste (MSW), the specific characteristics and contributions of inorganic chlorine in different MSW categories were investigated in this study. MSW samples were collected from eight representative waste classification residential areas in Hangzhou, China. It was found that the inorganic chlorine content in different MSW categories varied significantly (0-113 mg/g). Perishable waste, paper, and plastic were found to be the main sources of inorganic chlorine in MSW. A four-category classification system was used to quantify the contribution of inorganic chlorine from each waste category. It was found that the misclassification of inorganic chlorine contributions from perishable waste and other waste accounted for 51.96% and 48.04%, respectively. However, when correctly classified into the four-category system, their contributions were reduced to 67.14% and 30.65%, respectively. Therefore, MSW classification showed a significant reduction in the overall contribution of inorganic chlorine. The misclassification reduces the contribution of inorganic chlorine to 48.04%, while correct classification increases the reduction to 69.35%.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.

BACKGROUND: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

The Evolving Clinical Trajectory From a Sliding Hiatus Hernia to Mixed Sliding and Para-Oesophageal Subtype: A Case Report.

We present a compelling case of a patient initially diagnosed with a simple sliding hiatus hernia (HH), which was managed conservatively through optimised medical therapy. Over the span of a few years, she developed new symptoms which included epigastric discomfort and pain, prompting further clinical review and imaging investigation. These revealed the progression of her HH from a simple form to a more complex rolling or para-oesophageal type. This outcome highlights the importance of recognising a potential for progression during the clinical assessment of patients with a history of reflux symptoms and the onset of new epigastric discomfort or pain. Understanding this continuum of HHs is essential for physicians as management plans may need to switch from a conservative to a more invasive approach.

Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.

Ultrasound diagnosis of cystic echinococcosis: updates and implications for clinical management.

The diagnosis of cystic echinococcosis (CE) is based on imaging. Detection of a focal lesion with morphological characteristics of Echinococcus granulosus sensu lato metacestode is the starting point for the diagnostic workup. In organs explorable with ultrasound (US), this is the method of choice for both aetiological diagnosis of CE and staging of the CE cyst. Staging in terms of lesion morphology is also needed when serology is added to the diagnostic workflow when imaging alone is inconclusive. Finally, staging guides the clinical management of uncomplicated CE, especially in the liver. This commentary provides an overview of the most up-to-date evidence backing the above-mentioned role of US in the diagnosis and clinical management of CE. Finally, we outline future perspectives for the improvement of CE diagnosis.

Current advances and future directions in combined hepatocellular and cholangiocarcinoma.

The low incidence of combined hepatocellular cholangiocarcinoma (cHCC-CCA) is an important factor limiting research progression. Our study extensively included nearly three decades of relevant literature and assembled the most comprehensive database comprising 5,742 patients with cHCC-CCA. We summarized the characteristics, tumor markers, and clinical features of these patients. Additionally, we present the evolution of cHCC-CCA classification and explain the underlying rationale for these classification standards. We reviewed cHCC-CCA diagnostic advances using imaging features, tumor markers, and postoperative pathology, as well as treatment options such as surgical, adjuvant, and immune-targeted therapies. In addition, recent advances in more effective chemotherapeutic regimens and immune-targeted therapies were explored. Furthermore, we described the molecular mutation features and potential specific markers of cHCC-CCA. The prognostic value of Nestin has been proven, and we speculate that Nestin will also play a role in classification and diagnosis. However, further research is needed. Moreover, we believe that the possibility of using machine learning liquid biopsy for preoperative diagnosis and establishing a scoring system are directions for future research.

Classifying contaminated cell cultures using time series features.

We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation.

An early force prediction control scheme using multimodal sensing of electromyography and digit force signals.

Different grasping gestures result in the change of muscular activity of the forearm muscles. Similarly, the muscular activity changes with a change in grip force while grasping the object. This change in muscular activity, measured by a technique called Electromyography (EMG) is used in the upper limb bionic devices to select the grasping gesture. Previous research studies have shown gesture classification using pattern recognition control schemes. However, the use of EMG signals for force manipulation is less focused, especially during precision grasping. In this study, an early predictive control scheme is designed for the efficient determination of grip force using EMG signals from forearm muscles and digit force signals. The optimal pattern recognition (PR) control schemes are investigated using three different inputs of two signals: EMG signals, digit force signals and a combination of EMG and digit force signals. The features extracted from EMG signals included Slope Sign Change, Willison Amplitude, Auto Regressive Coefficient and Waveform Length. The classifiers used to predict force levels are Random Forest, Gradient Boosting, Linear Discriminant Analysis, Support Vector Machines, k-nearest Neighbors and Decision Tree. The two-fold objectives of early prediction and high classification accuracy of grip force level were obtained using EMG signals and digit force signals as inputs and Random Forest as a classifier. The earliest prediction was possible at 1000 ms from the onset of the gripping of the object with a mean classification accuracy of 90 % for different grasping gestures. Using this approach to study, an early prediction will result in the determination of force level before the object is lifted from the surface. This approach will also result in better biomimetic regulation of the grip force during precision grasp, especially for a population facing vision deficiency.