(1) Background: Peripheral nerve injuries have a great impact on a patient’s quality of life and a generally poor outcome regarding functional recovery. Lately, studies have focused on different types of nanoparticles and various natural substances for the treatment of peripheral nerve injuries. This is the case of chitosan, a natural compound from the crustaceans’ exoskeleton. The present study proposes to combine chitosan benefic properties to the nanoparticles’ ability to transport different substances to specific locations and evaluate the effects of magnetic nanoparticles functionalized with chitosan (CMNPs) on peripheral nerve injuries’ rehabilitation by using an in vivo experimental model. (2) Methods: CMNPs treatment was administrated daily, orally, for 21 days to rats subjected to right sciatic nerve lesion and compared to the control group (no treatment) by analyzing the sciatic functional index, pain level, body weight, serum nerve growth factor levels and histology, TEM and EDX analysis at different times during the study. (3) Results: Animals treated with CMNPs had a statistically significant functional outcome compared to the control group regarding: sciatic functional index, pain-like behavior, total body weight, which were confirmed by the histological and TEM images. (4) Conclusions: The results of the study suggest that CMNPs appear to be a promising treatment method for peripheral nerve injuries.

Background: In recent years, numerous artificial intelligence applications, especially generative large language models, have evolved in the medical field. This study conducted a structured comparative analysis of four leading generative large language models (LLMs)—ChatGPT-4o (OpenAI), Grok-3 (xAI), Gemini-2.0 Flash (Google), and DeepSeek-V3 (DeepSeek)—to evaluate their diagnostic performance in clinical case scenarios. Methods: We assessed medical knowledge recall and clinical reasoning capabilities through staged, progressively complex cases, with responses graded by expert raters using a 0–5 scale. Results: All models performed better on knowledge-based questions than on reasoning tasks, highlighting the ongoing limitations in contextual diagnostic synthesis. Overall, DeepSeek outperformed the other models, achieving significantly higher scores across all evaluation dimensions (p < 0.05), particularly in regards to medical reasoning tasks. Conclusions: While these findings support the feasibility of using LLMs for medical training and decision support, the study emphasizes the need for improved interpretability, prompt optimization, and rigorous benchmarking to ensure clinical reliability. This structured, comparative approach contributes to ongoing efforts to establish standardized evaluation frameworks for integrating LLMs into diagnostic workflows.

Background/Objectives: Diagnostic accuracy studies are essential for the evaluation of the performance of medical tests. The risk of bias (RoB) for these studies is commonly assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool. This study aimed to assess the capabilities and reasoning accuracy of large language models (LLMs) in evaluating the RoB in diagnostic accuracy studies, using QUADAS 2, compared to human experts. Methods: Four LLMs were used for the AI assessment: ChatGPT 4o model, X.AI Grok 3 model, Gemini 2.0 flash model, and DeepSeek V3 model. Ten recent open-access diagnostic accuracy studies were selected. Each article was independently assessed by human experts and by LLMs using QUADAS 2. Results: Out of 110 signaling questions assessments (11 questions for each of the 10 articles) by the four AI models, and the mean percentage of correct assessments of all the models was 72.95%. The most accurate model was Grok 3, followed by ChatGPT 4o, DeepSeek V3, and Gemini 2.0 Flash, with accuracies ranging from 74.45% to 67.27%. When analyzed by domain, the most accurate responses were for “flow and timing”, followed by “index test”, and then similarly for “patient selection” and “reference standard”. An extensive list of reasoning errors was documented. Conclusions: This study demonstrates that LLMs can achieve a moderate level of accuracy in evaluating the RoB in diagnostic accuracy studies. However, they are not yet a substitute for expert clinical and methodological judgment. LLMs may serve as complementary tools in systematic reviews, with compulsory human supervision.

Background and objectives: Informed decision-making requires the ability to identify and integrate high-quality scientific evidence in daily practice. We aimed to assess whether randomized controlled trials (RCTs) on endometriosis therapy follow methodological criteria corresponding to the RCTs’ specific level in the hierarchy of evidence in such details to allow the reproduction and replication of the study. Materials and Methods: Using the keywords “therapy” and “endometriosis” and “efficacy” three bibliographic databases were searched for English written scientific articles published from 1 January 2008 to 3 March 2018. Only the randomized clinical trials (RCTs) were evaluated in terms of whether they provided the appropriate level of scientific evidence, equivalent to level 1, degree 1b in the hierarchy of evidence. A list of criteria to ensure study replication and reproduction, considering CONSORT guideline and MECIR standards, was developed and used to evaluate RCTs’ methodological soundness, and scores were granted. Three types of bias, namely selection bias (random sequence generation and allocation concealment), detection bias (blinding of outcome assessment), and attrition bias (incomplete outcome data) were also evaluated. Results: We found 387 articles on endometriosis therapy, of which 38 were RCTs: 30 double-blinded RCTs and 8 open-label RCTs. No article achieved the maximum score according to the evaluated methodological criteria. Even though 73.3% of the double-blinded RCTs had clear title, abstract, introduction, and objectives, only 13.3% provided precise information regarding experimental design and randomization, and also showed a low risk of bias. The blinding method was poorly reported in 43.3% of the double-blinded RCTs, while allocation concealment and random sequence generation were inadequate in 33.3% of them. Conclusions: None of the evaluated RCTs met all the methodological criteria, none had only a low risk of bias and provided sufficient details on methods and randomization to allow for the reproduction and replication of the study. Consequently, the appropriate level of scientific evidence (level 1, degree 1b) could not be granted. On endometriosis therapy, this study evaluated the quality of reporting in RCTs and not the quality of how the studies were performed.

Some studies have reported that chronic respiratory illnesses in patients with COVID-19 result in an increase in hospitalization and death rates, while other studies reported to the contrary. The present research aims to determine if a predictive model (developed by combing different clinical, imaging, or blood markers) could be established for patients with both chronic obstructive pulmonary disease (COPD) and COVID-19, in order to be able to foresee the outcomes of these patients. A prospective observational cohort of 165 patients with both diseases was analyzed in terms of clinical characteristics, blood tests, and chest computed tomography results. The beta-coefficients from the logistic regression were used to create a score based on the significant identified markers for poor outcomes (transfers to an intensive care unit (ICU) for mechanical ventilation, or death). The severity of COVID-19, renal failure, diabetes, smoking status (current or previous), the requirement for oxygen therapy upon admission, high lactate dehydrogenase (LDH) and C-reactive protein level (CRP readings), and low eosinophil and lymphocyte counts were all identified as being indicators of a poor prognosis. Higher mortality was linked to the occurrence of renal failure, the number of affected lobes, the need for oxygen therapy upon hospital admission, high LDH, and low lymphocyte levels. Patients had an 86.4% chance of dying if their mortality scores were −2.80 or lower, based on the predictive model. The factors that were linked to a poor prognosis in patients who had both COPD and COVID-19 were the same as those that were linked to a poor prognosis in patients who had only COVID-19.

Purpose: To assess the assumption that differences exist between the traditional and publication-based PhD routes in terms of the thesis’ length and the scientific publications originating from it. Method: A retrospective comparative study on medical PhD theses offered by an online repository was performed. All free full-text medical PhD theses defended at United Kingdom institutions between 2003 and 2015 were analyzed and assigned to the traditional (TT) or publication based thesis (PBT) group. Several characteristics of theses and thesis-related articles were collected and analyzed. The thesis-related articles were investigated regarding quantity and visibility (citations, impact factor, and journal rank). Results: The theses length proved similar in PBT and TT group. PBT group included significantly more studies than TT group (mean 4.44 vs. 2.67) also reflected in significantly more thesis-related articles. The percentage of articles listed in Web of Science and published in a journal with impact factor proved significantly lower in TT compared with PBT group. On the contrary, article citations were significantly higher for TT. Both groups published similarly in high-ranked journals (Q1 or Q2). Conclusion: The research productivity originating from the PBT group was, as expected, significantly larger but not significantly more visible than those from TT group.