Current German Recommendations and International Research on the Use of COVID-19 Boosters among Health Care Providers in 2024: A Narrative Review.

While the World Health Organization (WHO) has de-escalated coronavirus disease 2019 (COVID-19) from a global health emergency, ongoing discussions persist as new viral variants. This article aimed to consolidate German recommendations and international research to offer health care providers (HCPs) a comprehensive guide on COVID-19 boosters in 2024. The review outlines key recommendations from the German Robert Koch Institute. HCPs should receive COVID-19 boosters at least 12 months after their last vaccination or COVID-19 infection, contingent on the prevalent viral variant(s) in the region. However, excessive doses and/or frequent boosters, especially with mRNA vaccines, may lead to immune imprinting, T-cell exhaustion, and immunoglobulin (Ig) switching. Notably, this review highlights the significance of Ig, particularly IgA and IgG subclasses, in influencing infection risk and disease progression. Furthermore, it explores the implications of mRNA vaccine technology and potential adverse effects related to excessive dosing. In conclusion, this article provides a comprehensive analysis of COVID-19 vaccine boosters for HCPs, synthesising current recommendations, scientific debates, and considerations for optimising protection against SARS-CoV-2 in the evolving landscape of the post-pandemic era.

Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Finetuning of GLIDE stable diffusion model for AI-based text-conditional image synthesis of dermoscopic images.

Background: The development of artificial intelligence (AI)-based algorithms and advances in medical domains rely on large datasets. A recent advancement in text-to-image generative AI is GLIDE (Guided Language to Image Diffusion for Generation and Editing). There are a number of representations available in the GLIDE model, but it has not been refined for medical applications. Methods: For text-conditional image synthesis with classifier-free guidance, we have fine-tuned GLIDE using 10,015 dermoscopic images of seven diagnostic entities, including melanoma and melanocytic nevi. Photorealistic synthetic samples of each diagnostic entity were created by the algorithm. Following this, an experienced dermatologist reviewed 140 images (20 of each entity), with 10 samples originating from artificial intelligence and 10 from original images from the dataset. The dermatologist classified the provided images according to the seven diagnostic entities. Additionally, the dermatologist was asked to indicate whether or not a particular image was created by AI. Further, we trained a deep learning model to compare the diagnostic results of dermatologist versus machine for entity classification. Results: The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The integration of synthetic images improved the classification performance of the model, resulting in higher accuracy and precision. The AI assessment showed superior classification performance compared to dermatologist. Conclusion: Overall, the results highlight the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity.

Associations between Periodontitis and COPD: An Artificial Intelligence-Based Analysis of NHANES III.

A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.

Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.

BACKGROUND: Extended skin malignancies of the head and neck region are among the most common cancer types and are associated with numerous diagnostic and therapeutical problems. The radical resection of skin cancer in the facial area often leads to severe functional and aesthetic impairment, and precise margin assessments can avoid the extensive safety margins. On the other hand, the complete removal of the cancer is essential to minimize the risk of recurrence. Reliable intraoperative assessments of the wound margins could overcome this discrepancy between minimal invasiveness and safety distance in the head and neck region. With the help of reflectance confocal laser microscopy (RCM), cells can be visualized in high resolution intraoperatively. The combination with deep learning and automated algorithms allows an investigator independent and objective interpretation of specific confocal imaging data. Therefore, we aimed to apply a deep learning algorithm to detect malignant areas in images obtained via in vivo confocal microscopy. We investigated basal cell carcinoma (BCC), as one of the most common entities with well-described in vivo RCM diagnostic criteria, within a preliminary feasibility study. PATIENTS AND METHODS: We included 62 patients with histologically confirmed BCC in the head and neck region. All patients underwent in vivo confocal laser microscope scanning. Approximately 382 images with BCC structures could be obtained, annotated, and proceeded for further deep learning model training. RESULTS: A sensitivity of 46% and a specificity of 85% in detecting BCC regions could be achieved using a convolutional neural network model ("MobileNet"). CONCLUSION: The preliminary results reveal the potential and limitations of the automated detection of BCC with in vivo RCM. Further studies with a larger number of cases are required to obtain better predictability.

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models.

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

Ex vivo fluorescent confocal microscopy images of oral mucosa: Tissue atlas and evaluation of the learning curve.

Ex vivo fluorescence confocal microscopy (FCM) is a developing tool providing rapid digital imaging of fresh tissue utilizing high-resolution optical sectioning that highly corresponds with conventional hmatoxylin and eosin (H&E)-stained slides. A very little data on oral mucosa lesions exist currently. The present work aimed to create an image atlas of benign and malignant oral tissues and compare them to the corresponding histopathology. Furthermore, we aimed to evaluate the learning curve for confocal image interpretation. From 50 samples obtained from the oral mucosa, including oral squamous cell carcinoma (OSCC), dysplasia, and healthy oral tissue, ex vivo FCM images and corresponding H&E slides were created and collected into a tissue atlas. Additionally, two experts were asked to analyze the images to assess the learning curve. Ex vivo FCM images revealed high comparability with histopathological images. Tissues including OSCC, dysplasia, and normal oral mucosa were implemented in the image atlas to provide the diagnostic fundament for pathologists and surgeons; the learning curve was short. Future studies on this topic will be advantageous for the development of artificial intelligence-based diagnostic approaches. The current work provides a novel set of data that are structured as an atlas of common pathologies of the mucosa to enhance the existing knowledge and material on confocal images.

Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study.

BACKGROUND: Ex vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. MATERIAL AND METHODS: Tissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy. RESULTS: The model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study. CONCLUSION: In this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.

Health-related quality of life in children after surgical treatment of non-syndromal craniosynostosis.

PURPOSE: Craniosynostosis is a premature ossification of the fibrous sutures in a skull which results in a changing of the growth pattern of the skull with abnormal head shapes, midface hypoplasia, and neurofunctional disorders. Surgical correction of craniosynostosis by opening the ossified cranial sutures is usually made in the infant phase to avoid compression of the brain and mental deficits. However, little is known regarding the health-related quality of life among children after surgical treatment of diverse forms of craniosynostosis. The purpose of this study was to evaluate the quality of life of adolescent patients who anderwent a surgical correction of non-syndromal craniosynostosis in infancy. MATERIALS AND METHODS: The study population included 48 adolescents with an average age of 12.1 ± 4.3 years. The KINDL questionnaire, which was specifically validated for children, was used to estimate different dimensions of quality of life and the impact of various factors on it. RESULTS: In the investigated group, boys were more likely to be affected by non-syndromal craniosynostosis than were girls. Children and their parents showed a high degree of correlation in each of the investigated dimensions of the KINDL questionnaire. Sex, the type of craniosynostosis, surgical technique, and surgical result did not seem to have an influence on the quality of life. A negative correlation between the time of surgery and the family-related quality of life could be shown. None of the patients in the study had relevant limitations or impairments in their later life. CONCLUSIONS: In our study, patients with simple non-syndromic craniosynostoses who anderwent operative correction of craniosynostosis in infancy do not show any quality-of-life limitations in their later life compared to the average population.

Mandibular Brown Tumor as a Result of Secondary Hyperparathyroidism: A Case Report with 5 Years Follow-Up and Review of the Literature.

BACKGROUND: Brown tumor is a rare skeletal manifestation of secondary hyperparathyroidism. Although diagnosis of the disease is increasingly seen in early stages due to improved screening techniques, some patients still present in a progressed disease stage. The treatment depends on tumor mass and varies from a conservative approach with supportive parathyroidectomy to extensive surgical resection with subsequent reconstruction. CASE PRESENTATION: We report a case of extensive mandibular brown tumor in a patient with a history of systemic lupus erythematosus, chronic kidney disease, and secondary hyperparathyroidism. Following radical resection of the affected bone, reconstruction could be successfully performed using a free flap. CONCLUSIONS: There were no signs of recurrence during five years of close follow-up. Increased awareness and multidisciplinary follow-ups could allow early diagnosis and prevent the need for radical therapeutical approaches.

Feasibility and Implementation of Ex Vivo Fluorescence Confocal Microscopy for Diagnosis of Oral Leukoplakia: Preliminary Study.

BACKGROUND: Oral leukoplakia is a potentially malignant lesion with a clinical impression similar to different benign and malignant lesions. Ex vivo fluorescence confocal microscopy is a developing approach for a rapid "chairside" detection of oral lesions with a cellular-level resolution. A possible application of interest is a quick differentiation of benign oral pathology from normal or cancerous tissue. The aim of this study was to analyze the sensitivity and specificity of ex vivo fluorescence confocal microscopy (FCM) for detecting oral leukoplakia and to compare confocal images with gold-standard histopathology. METHODS: Imaging of 106 submosaics of 27 oral lesions was performed using an ex vivo fluorescence confocal microscope immediately after excision. Every confocal image was qualitatively assessed for presence or absence of leukoplakia by an expert reader of confocal images. The results were compared to conventional histopathology with H&E staining. RESULTS: Leukoplakia was detected with an overall sensitivity of 96.3%, specificity of 92.3%, positive predictive value of 93%, and negative predictive value of 96%. CONCLUSION: The results demonstrate the potential of ex vivo confocal microscopy in fresh tissue for rapid real-time assessment of oral pathologies.

Features of oral squamous cell carcinoma in ex vivo fluorescence confocal microscopy.

BACKGROUND: Real-time microscopic imaging of freshly excised oral squamous cell carcinomas (OSCCs) would be potentially supportive in rapid recognition of oral malignancy and an optimal and time-saving management of patients' surgical treatment. OBJECTIVES: The aim of this study was to examine oral squamous cell cancer tissue in regards to the commonly known and well-described histomorphologic criteria for the diagnosis of OSCC in ex vivo confocal fluorescent microscopy and to analyze its correlation with grade of differentiation and level of invasiveness. METHODS: Ex vivo confocal laser scanning microscopy (CLSM) images of 38 OSCCs were evaluated immediately after excision for presence or absence of various cytological and architectural features based on the histopathological background. Next, these features were compared to the grade of differentiation as elaborated via gold standard histologic examination. RESULTS: Of 38 invasive OSCCs, 14 were well differentiated, while three moderately and 19 were poorly differentiated. The presence of the commonly known cytologic and histopathologic criteria for the diagnosis of oral squamous cell carcinoma such as the destruction of the basal cell membrane, cellular and nuclear pleomorphism, anisocytosis, intraepithelial keratinization, nuclear hyperchromasia, atypical mitotic figures as well as the presence of necrosis, and mixed inflammation could be observed in ex vivo fluorescence confocal microscopy (FCM). In ex vivo fluorescence confocal microscopy pictures, cellular pleomorphism and anisocytosis were observed more often in poorly differentiated OSCCs. Intraepithelial keratinization was associated with well differentiated and moderately differentiated OSCCs. CONCLUSION: The results demonstrate the high potential of ex vivo fluorescence confocal microscopy in fresh tissue for rapid real-time diagnosis of OSCC.

In vivo reflectance confocal microscopy of wounds: feasibility of intraoperative basal cell carcinoma margin assessment.

BACKGROUND: In vivo reflectance confocal microscopy (RCM) is well established in non-melanoma skin cancer detection and screening. However, there is no sufficient validation regarding intraoperatively obtained images of wound margins. A reliable and fast resection margin detection is of high clinical relevance. Hence, we aimed to investigate feasibility and validity of in vivo RCM imaging for wound margins assessment compared with standard skin surface imaging and the gold standard histopathology. METHODS: A surgical incision through the center of a large basal cell carcinoma (BCC) affected area in the head and face region was performed. After removing half of the tumor, the wound margins of the remaining half as well as the corresponding skin surface were scanned with an in vivo RCM. A total of 50 wound margin images with BCC, 50 images of BCC-free margins and the corresponding skin surface images from 50 patients were compared with each other and with histopathological findings. Presence of confocal diagnostic criteria for BCC in images was analyzed. RESULTS: An overall sensitivity and specificity in detection of BCC in wound margins was 88.5%, and 91.7% compared to skin surface imaging and 97.8% and 90.7%, respectively, compared to histopathology. We identified all known confocal patterns of healthy skin and BCC in wound margin scans: damage of the epidermal layer above the lesion and cellular pleomorphism, elongated and monomorphic basaloid nuclei, nuclear polarization, an increased number of dilated blood vessels with high leukocyte traffic, inflammatory cells. CONCLUSIONS: The accuracy of in vivo RCM imaging of wound margins is comparable with a standard skin surface imaging. The intraoperative detection of BCC areas in wound margins is as precise as the standard skin imaging and may be supportive for surgical interventions.

Wound closure and alveoplasty after preventive tooth extractions in patients with antiresorptive intake-A randomized pilot trial.

OBJECTIVES: To compare success rates between the sub-periosteal prepared (SPP) muco-periosteal flap and the epi-periosteal prepared (EPP) mucosa flap and the feasibility of alveoplasty after surgical tooth extractions in patients undergoing/after antiresorptive treatment. SUBJECTS: Patients with an indication for preventive tooth extraction undergoing/after antiresorptive treatment were enrolled over a 24-month period in a parallel-group randomized clinical pilot trial and randomly assigned for primary wound closure to either the SPP or the EPP group. The primary outcome was treatment failure 8 weeks after surgery. To assess the feasibility of alveoplasty, necrotic bone changes at the time point of tooth extraction were evaluated. RESULTS: One hundred and sixty patients were randomized to the SSP (n = 82) or the EPP (n = 78) group. One hundred and fifty-seven patients met the primary endpoint 8 weeks after surgery with five treatment failures for the SPP group (6.3%) and 18 (23.4%) for the EPP group (p = .004). A significant relationship (p < .0001) was observed between symptomatic teeth and non-vital bone found in 54.8% of all biopsies. CONCLUSIONS: The strong superiority of the muco-periosteal flap as primary wound closure revealed the feasibility and effectiveness of the study. The large number of necrotic biopsies emphasizes the importance of alveoplasty as a preventive measure.

Detection of oral squamous cell carcinoma with ex vivo fluorescence confocal microscopy: Sensitivity and specificity compared to histopathology.

Real-time microscopic imaging of freshly excised tissue enables a rapid bedside-pathology. A possible application of interest is the detection of oral squamous cell carcinomas (OSCCs). The aim of this study was to analyze the sensitivity and specificity of ex vivo fluorescence confocal microscopy (FCM) for OSCCs and to compare confocal images visually and qualitatively with gold standard histopathology. Two hundred eighty ex vivo FCM images were prospectively collected and evaluated immediately after excision. Every confocal image was blindly assessed for the presence or absence of malignancy by two clinicians and one pathologist. The results were compared with conventional histopathology with hematoxylin and eosin staining. OSCCs were detected with a very high sensitivity of 0.991, specificity of 0.9527, positive predictive value of 0.9322 and negative predictive value of 0.9938. The results demonstrate the potential of ex vivo FCM in fresh tissue for rapid real-time surgical pathology.

Do Functional Scores of Flap Donor Sites Recover After Reconstruction of Segmental Jaw Defects?

PURPOSE: The potential for donor-site morbidity in major maxillofacial reconstruction remains a concern. The purpose of this study was to compare the outcome of donor-site morbidity of deep circumflex iliac artery (DCIA) and scapula free flaps after radical treatment of the jaw and flap reconstruction. PATIENTS AND METHODS: We implemented a prospective cohort study design. Patients requiring segmental resection for benign pathology underwent reconstruction with either DCIA or scapula free flaps. The primary predictor variable was the use of DCIA versus scapula free flaps. The primary outcome variables were changes in orthopedic functional scores for both donor sites. The secondary outcome variable was neurosensory recovery at the recipient site. RESULTS: We included 8 patients in this study, comprising 3 women (38%) and 5 men (62%). Orthopedic scores were assigned preoperatively (T0) and at follow-up appointments at 1 to 3 months postoperatively (T1) and 6 to 12 months postoperatively (T2). In patients with DCIA flaps, a significant reduction (P = .0096) in the Larson I score between examination time points T0 and T1 was found. The score then improved on the operated side between T1 and T2 by an average of 29 points and showed no significant difference compared with the T0 level (P = .68). Patients with a scapular graft showed a significant reduction (P = .004) in the Constant-Murley score on the operated side between T0 and T1. The Constant-Murley score again improved significantly (P = .0136) between T1 and T2. Most of the patients (n = 7, 88%) had a neurologically unremarkable local situation of the recipient site at T0. At T1, 1 patient had level A (mild) neurosensory disorder and 1 had level B (moderate). At T2, all patients' initial neurologic scores were restored. CONCLUSIONS: The donor-site morbidity associated with DCIA and scapula flap reconstruction is a short-term condition and returns to baseline by 3 to 6 months postoperatively.

Visualization and quantification of tongue movement during articulation: Is ultrasound a valid alternative to magnetic resonance imaging?

BACKGROUND: Quality of life in tumour patients following combined ablative and reconstructive head and neck surgery varies significantly. This is at least partially due to differences in speech and swallowing function. A tool to objectively evaluate articulation by magnetic resonance imaging (MRI) has now been tested alongside ultrasound examination. PATIENTS AND METHODS: A standardized sequence of phonemes from the German language was established in 20 healthy volunteers and recorded in different planes by MRI and ultrasound. Phonemes were identified through recognition of typical tongue configurations by two different observers. An algorithm for metric analysis of articulation in terms of distances and angles for five extreme points on the tongue (anterior, posterior, cranial and two basal corners) was designed. The findings in these volunteers were subsequently compared with the results of an examination of a tumour patient. RESULTS: Physiological articulation was visualized and evaluated both by MRI and ultrasound. There was a high intra-class correlation coefficient for measurements between independent observers. Tongue position for certain phonemes was mostly constant in healthy patients of different age groups, with gender-specific differences. In a first comparison, tongue position in a patient with tongue cancer differed significantly from this position, both pre- and postoperatively. In agreement with clinical articulation quality, the tongue position of the patient returned to almost normal within 12 months postoperatively. CONCLUSION: Both ultrasound and MRI are appropriate instruments for visualization of articulation and objective measurements to evaluate speech in tumour patients. Whilst MRI is more precise and can identify more subtle differences, ultrasound is a valid alternative due to its wider availability and broader applicability.