Functional and Disability Outcomes in NSCLC Patients Post-Lobectomy Undergoing Pulmonary Rehabilitation: A Biopsychosocial Approach.

Worldwide, lung cancer remains the predominant cause of cancer cases and deaths and poses significant health challenges, with surgical resection being a key treatment. Post-surgery, patients often experience functional impairments. This study aimed to develop a comprehensive ICF version for assessing the functional profile and disability in lung cancer patients post-thoracic surgery undergoing pulmonary rehabilitation using the ICF and WHODAS 2.0 tool. We analyzed the correlation between the ICF Core Set and WHODAS 2.0 data to understand the impact on daily functioning. This study included 50 patients (23 F, 27 M) from the Clinic of Thoracic Surgery and Respiratory Rehabilitation in Lodz. Essential ICF codes were determined using the Delphi method, and assessments were conducted on the third day post-operation. Statistical analyses included various tests with α = 0.05. The results showed no impairments in voice functions (b310), respiration rates (b4400), and diaphragm functions (b4451), but there were significant issues with chest pain (b28011), respiratory muscle functions (b445), exercise tolerance (b455), and muscle endurance (b740). In Activities and Participation and Environmental Factors, most codes were not problematic, except for employment (d845, d850) and atmospheric pressure (e2252). Significant correlations were found between mobility limitations (d410, d460) and self-care (d510, d540) with the WHODAS 2.0 results. The comprehensive ICF Core Set effectively described the functional profile of post-surgery patients, confirming its utility and highlighting the impact of disability on daily functioning.

Domains in Action: Understanding Ddi1's Diverse Functions in the Ubiquitin-Proteasome System.

The ubiquitin-proteasome system (UPS) is a pivotal cellular mechanism responsible for the selective degradation of proteins, playing an essential role in proteostasis, protein quality control, and regulating various cellular processes, with ubiquitin marking proteins for degradation through a complex, multi-stage process. The shuttle proteins family is a very unique group of proteins that plays an important role in the ubiquitin-proteasome system. Ddi1, Dsk2, and Rad23 are shuttle factors that bind ubiquitinated substrates and deliver them to the 26S proteasome. Besides mediating the delivery of ubiquitinated proteins, they are also involved in many other biological processes. Ddi1, the least-studied shuttle protein, exhibits unique physicochemical properties that allow it to play non-canonical functions in the cells. It regulates cell cycle progression and response to proteasome inhibition and defines MAT type of yeast cells. The Ddi1 contains UBL and UBA domains, which are crucial for binding to proteasome receptors and ubiquitin respectively, but also an additional domain called RVP. Additionally, much evidence has been provided to question whether Ddi1 is a classical shuttle protein. For many years, the true nature of this protein remained unclear. Here, we highlight the recent discoveries, which shed new light on the structure and biological functions of the Ddi1 protein.

Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment.

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially in supporting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in artificial intelligence (AI) for diagnostic purposes, we conducted a study evaluating the capabilities of AI models, including ChatGPT and Microsoft Bing, in the diagnosis of single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed the degree of spinal deformation, selecting 23 cases of severe single-curve scoliosis. Each posturographic image was separately implemented onto each of the mentioned platforms using a set of formulated questions, starting from 'What do you see in the image?' and ending with a request to determine the Cobb angle. In the responses, we focused on how these AI models identify and interpret spinal deformations and how accurately they recognize the direction and type of scoliosis as well as vertebral rotation. The Intraclass Correlation Coefficient (ICC) with a 'two-way' model was used to assess the consistency of Cobb angle measurements, and its confidence intervals were determined using the F test. Differences in Cobb angle measurements between human assessments and the AI ChatGPT model were analyzed using metrics such as RMSEA, MSE, MPE, MAE, RMSLE, and MAPE, allowing for a comprehensive assessment of AI model performance from various statistical perspectives. The ChatGPT model achieved 100% effectiveness in detecting scoliosis in X-ray images, while the Bing model did not detect any scoliosis. However, ChatGPT had limited effectiveness (43.5%) in assessing Cobb angles, showing significant inaccuracy and discrepancy compared to human assessments. This model also had limited accuracy in determining the direction of spinal curvature, classifying the type of scoliosis, and detecting vertebral rotation. Overall, although ChatGPT demonstrated potential in detecting scoliosis, its abilities in assessing Cobb angles and other parameters were limited and inconsistent with expert assessments. These results underscore the need for comprehensive improvement of AI algorithms, including broader training with diverse X-ray images and advanced image processing techniques, before they can be considered as auxiliary in diagnosing scoliosis by specialists.

Artificial Intelligence in Scoliosis Classification: An Investigation of Language-Based Models.

Open-source artificial intelligence models are finding free application in various industries, including computer science and medicine. Their clinical potential, especially in assisting diagnosis and therapy, is the subject of increasingly intensive research. Due to the growing interest in AI for diagnostics, we conducted a study evaluating the abilities of AI models, including ChatGPT, Microsoft Bing, and Scholar AI, in classifying single-curve scoliosis based on radiological descriptions. Fifty-six posturographic images depicting single-curve scoliosis were selected and assessed by two independent neurosurgery specialists, who classified them as mild, moderate, or severe based on Cobb angles. Subsequently, descriptions were developed that accurately characterized the degree of spinal deformation, based on the measured values of Cobb angles. These descriptions were then provided to AI language models to assess their proficiency in diagnosing spinal pathologies. The artificial intelligence models conducted classification using the provided data. Our study also focused on identifying specific sources of information and criteria applied in their decision-making algorithms, aiming for a deeper understanding of the determinants influencing AI decision processes in scoliosis classification. The classification quality of the predictions was evaluated using performance evaluation metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and balanced accuracy. Our study strongly supported our hypothesis, showing that among four AI models, ChatGPT 4 and Scholar AI Premium excelled in classifying single-curve scoliosis with perfect sensitivity and specificity. These models demonstrated unmatched rater concordance and excellent performance metrics. In comparing real and AI-generated scoliosis classifications, they showed impeccable precision in all posturographic images, indicating total accuracy (1.0, MAE = 0.0) and remarkable inter-rater agreement, with a perfect Fleiss' Kappa score. This was consistent across scoliosis cases with a Cobb's angle range of 11-92 degrees. Despite high accuracy in classification, each model used an incorrect angular range for the mild stage of scoliosis. Our findings highlight the immense potential of AI in analyzing medical data sets. However, the diversity in competencies of AI models indicates the need for their further development to more effectively meet specific needs in clinical practice.

Assessment of the Functioning Profile of Patients with Lung Cancer Undergoing Lobectomy in Relation to the ICF Rehabilitation Core Set.

Lung cancer often presents with pain and breathlessness, frequently necessitating surgical procedures, such as lung lobectomy. A pivotal component of postoperative care is rehabilitation, aimed not only at improving the clinical condition but also at influencing the patient's functional profile. In a study conducted at the Clinic of Thoracic Surgery and Respiratory Rehabilitation in the Regional Multispecialist Center for Oncology and Traumatology of the Nicolaus Copernicus Memorial Hospital in Lodz, the effectiveness of rehabilitation intervention was assessed in 50 patients (n = 27 M, n = 23 F) postlobectomy due to early stage nonsmall cell lung cancer (NSCLC). The International Classification of Functioning, Disability, and Health-ICF Rehabilitation Core Set was used to evaluate the functional profile, the modified Laitinen scale for pain assessment, and the modified Borg scale for breathlessness evaluation. Additionally, lung-expansion time was monitored. The significance level of the statistical tests in this analysis was set at α = 0.05. The study employed an analysis of the normality of the distributions of the numerical variables, reporting of variable distributions, estimation of differences between groups, estimation of differences within groups, estimation of the independence of categorical variables, and regression analysis. The research confirmed that rehabilitation partially improves the functional profile of patients and reduces the sensation of breathlessness postsurgery. The study highlighted the need for future research with a larger number of participants and an extended observation period to gain a deeper understanding of the impact of rehabilitation on patients after lung lobectomy procedures.

Evaluating Scoliosis Severity Based on Posturographic X-ray Images Using a Contrastive Language-Image Pretraining Model.

Assessing severe scoliosis requires the analysis of posturographic X-ray images. One way to analyse these images may involve the use of open-source artificial intelligence models (OSAIMs), such as the contrastive language-image pretraining (CLIP) system, which was designed to combine images with text. This study aims to determine whether the CLIP model can recognise visible severe scoliosis in posturographic X-ray images. This study used 23 posturographic images of patients diagnosed with severe scoliosis that were evaluated by two independent neurosurgery specialists. Subsequently, the X-ray images were input into the CLIP system, where they were subjected to a series of questions with varying levels of difficulty and comprehension. The predictions obtained using the CLIP models in the form of probabilities ranging from 0 to 1 were compared with the actual data. To evaluate the quality of image recognition, true positives, false negatives, and sensitivity were determined. The results of this study show that the CLIP system can perform a basic assessment of X-ray images showing visible severe scoliosis with a high level of sensitivity. It can be assumed that, in the future, OSAIMs dedicated to image analysis may become commonly used to assess X-ray images, including those of scoliosis.

Congenital Anterior Dislocation of the Sacrococcygeal Bone in a Newborn.

We present a case of a child who was transported to the Neurosurgery Clinic from another hospital for the purpose of performing a surgical procedure of the spinal myelomeningocele. On the first day of the stay, a set of tests was performed, including an anterior-posterior (AP) projection X-ray, which clearly showed a developmental defect in the lumbar-sacral section of the spine. In the follow-up physical examination, there was a depression of the skin on the right side of the surgical scar after closing the open myelomeningocele. In the follow-up MRI of the lumbar-sacral section, an extremely rare congenital anterior dislocation of the sacrococcygeal bone was unexpectedly visualized. Despite recommendations for further diagnostics, the patient did not attend the required follow-up examinations. In the final section, we provide a general summary of the literature on rare developmental defects of the spine in children.