Real Patient Research Articles

Digital Twins for More Precise and Personalized Treatment.

The use of Digital Twins (DTs) or the digital replicas of physical entities has provided benefits to several industry sectors, most notably manufacturing. To date, the application of DTs in the healthcare sector has been minimal, however. But, as pressure increases for more precise and personalized treatments, it behooves us to investigate the potential for DTs in the healthcare context. As a proof-of-concept demonstration prior to working with real patients, we attempt in this paper, to explore the potential for creating and using DTs. We do this in a synthetic environment at this stage, making use of data that is all computer-generated. DTs of synthetic present patients are created making use of data of synthetic past patients. In the real world, the clinical objective for creating such DTs of real patients would be to enable enhanced real-time clinical decision support to enable more precise and personalized care. The objective of the numerical experiment reported in this paper, is to envisage the possibilities and challenges of such an approach. We attempt to better understand the strengths and weaknesses of applying DTs in the healthcare context to support more precise and personalized treatments.

Development and validation of a new tool for assessment of trainees' interventional musculoskeletal ultrasound skills.

Interventional musculoskeletal ultrasound (MSUS) procedures are routinely performed in rheumatology practice. However, the efficacy and safety of the procedures rely on the competence of the physician, and assessment of skills is crucial. Thus, this study aimed to develop and establish validity evidence for a tool assessing trainees' interventional MSUS skills. An expert panel of rheumatologists modified an existing tool for assessing competences in invasive abdominal and thoracic ultrasound procedures. The new tool (the Assessment of Interventional Musculoskeletal Ultrasound Skills (AIMUS) tool) reflects the essential steps in interventional MSUS. To establish validity evidence, physicians with different levels of interventional MSUS experience were enrolled and performed two procedures on a rubber fantom, simulating real patient cases. All performances were video recorded, anonymized, and assessed in random order by two blinded raters using the AIMUS tool. Sixty-five physicians from 21 different countries were included and categorized into groups based on their experience, resulting in 130 videos for analysis. The internal consistency of the tool was excellent, with a Cronbach's alpha of 0.96. The inter-case reliability was good with a Pearson's correlation coefficient (PCC) of 0.74 and the inter-rater reliability was moderate to good (PCC 0.58). The ability to discriminate between different levels of experience was highly significant (p< 0.001). We have developed and established validity evidence for a new interventional MSUS assessment tool. The tool can be applied in future competency-based educational programs, provide structured feedback to trainees in daily clinical practice, and ensure end-of-training competence.

P150 Ulcerative Colitis Patients’ Luminal Zinc Induces Clostridioides difficile Virulence and Exacerbates Inflammatory Responses

Abstract Background The refractory acute severe UC patients are often complicated by C. difficile infection (CDI), resulting in higher surgery and mortality rates. However, few studies have observed C. difficile’s pathogenicity in real UC patients’ gastrointestinal microenvironments, and the actual role of C. difficile in UC development remains unknown. We found zinc concentrations among UC patients’ feces were significantly higher than those among Crohn’s disease patients and healthy subjects. Additionally, we previously reported that excessive zinc would promote the growth and virulence of C. difficile. Therefore, we hypothesized that the increased UC patients’ luminal zinc may up-regulate C. difficile virulence and promote intestinal inflammatory responses, leading to an exacerbated UC progression. Methods UC patients’ stool samples (n=40) were collected and analyzed by qPCR and ICP-MS to determine the presence of C. difficile and the fecal zinc concentrations. C. difficile was cultured in BHIs with or without zinc (ZnSO4) in an anaerobic chamber. Expression of virulence genes was analyzed using RT-qPCR, transcriptome sequencing, and western blot. Human colonic organoids and Caco-2 cells were co-cultured with C. difficile or zinc-treated C. difficile, and cytotoxicity, permeability, and inflammatory cytokine release were assessed. TLR5 knock-out Caco-2 cells were constructed using sgRNA-guided Cas9 nuclease and co-cultured with C. difficile or zinc-treated C. difficile to verify whether zinc-induced C. difficile’s pathogenicity was mainly through flagellar formation. Results C. difficile was detected in ~35% of UC patients’ fecal samples. Luminal zinc concentrations (~915μM, p&amp;lt;0.01) were significantly higher among UC patients than CD patients or healthy subjects. The UC patients’ luminal concentration of zinc promoted C. difficile growth, suppressed its exotoxins release, and induced higher expression of its flagella-related genes, resulting in higher cytotoxicity and inflammatory responses in human colonic organoids and Caco-2 cells. However, zinc-treated C. difficile did not induce higher inflammation and pathogenicity in TLR5 knock-out Caco-2 cells. Conclusion The association between C. difficile and UC aggravation may partially stem from UC patients’ excess luminal zinc promoting C. difficile flagellin expression, inducing inflammatory responses, and worsening UC progression.

Essential parameters needed for a U-Net-based segmentation of individual bones on planning CT images in the head and neck region using limited datasets for radiotherapy application.

Objective.The field of radiotherapy is highly marked by the lack of datasets even with the availability of public datasets. Our study uses a very limited dataset to provide insights on essential parameters needed to automatically and accurately segment individual bones on planning CT images of head and neck cancer patients.Approach.The study was conducted using 30 planning CT images of real patients acquired from 5 different cohorts. 15 cases from 4 cohorts were randomly selected as training and validation datasets while the remaining were used as test datasets. Four experimental sets were formulated to explore parameters such as background patch reduction, class-dependent augmentation and incorporation of a weight map on the loss function.Main results.Our best experimental scenario resulted in a mean Dice score of 0.93 ± 0.06 for other bones (skull, mandible, scapulae, clavicles, humeri and hyoid), 0.93 ± 0.02 for ribs and 0.88 ± 0.03 for vertebrae on 7 test cases from the same cohorts as the training datasets. We compared our proposed solution approach to a retrained nnU-Net and obtained comparable results for vertebral bones while outperforming in the correct identification of the left and right instances of ribs, scapulae, humeri and clavicles. Furthermore, we evaluated the generalization capability of our proposed model on a new cohort and the mean Dice score yielded 0.96 ± 0.10 for other bones, 0.95 ± 0.07 for ribs and 0.81 ± 0.19 for vertebrae on 8 test cases.Significance.With these insights, we are challenging the utilization of an automatic and accurate bone segmentation tool into the clinical routine of radiotherapy despite the limited training datasets.

P717 Evaluating the performance of Large Language Models in responding to patients' health queries: A comparative analysis with medical experts

Abstract Background Patients with chronic diseases exhibit a heightened interest in seeking health information, and access to high-quality information can positively impact clinical outcomes. While previous research on static internet text/video information has highlighted concerns about low-barrier creation leading to low-quality content, it remains uncertain whether similar issues persist in responses generated by Large Language Models (LLMs). Assessing the ability of LLMs in responding to medical queries provides valuable insights for their application in healthcare settings. Methods In alignment with open science principles, we utilized real patient queries from the China Crohn's and Colitis Foundation (CCCF) series "Questions and Answers on Ulcerative Colitis and Crohn's Disease." The dataset comprised questions posed by patients and corresponding answers from medical professionals, collected from outpatient visits and online social media. In September 2023, 263 patient questions were sequentially input into ChatGPT-3.5 (August 3, 2023 version), and the resulting responses were compiled alongside the original medical professional responses, forming 263 modules. Three Inflammatory Bowel Disease (IBD) specialist physicians and three IBD patients were invited to assess each module. Evaluators were instructed to: 1) choose their preferred response version, and 2) provide a multidimensional Likert 5-point subjective assessment using a crowdsourcing strategy. Additionally, the CRIE 3.0 team conducted an automated objective analysis of Simplified Chinese readability. Results Mann-Whitney U tests on text readability levels (median: 7th grade for both medical professionals and ChatGPT responses; Q1: 6th grade; Q3: 8th grade) revealed no significant difference (p=0.87), suggesting ChatGPT's performance align well with recommended literacy levels for popular science publications and is comparable to the average education level in China. Conclusion Cautiously interpreting our findings, ChatGPT's preliminary performance appears comparable to specialized IBD physicians, indicating its potential utility in patient community Q&amp;A. Integrating ChatGPT or similar LLMs into the drafting or refinement stages of health texts is feasible. However, due to the presence of AI hallucinations and the consensus in most experimental conclusions, direct use of large language models for patient Q&amp;A services is not recommended. Recognizing the variability in health information understanding between medical professionals and patients can enhance patient education efforts.

Students’ perception of Simulation-based learning in Clinical Skills Lab: A One-year institutional experience at Fazaia Ruth Pfau Medical College

Background: Clinical skills laboratory offers great potential for undergraduate medical education by introducing the students to innovative teaching and learning strategies that efficiently fill the gap between theoretical knowledge and clinical practice. Our study aimed to assess the initial perceptions and experiences of medical students for the newly established clinical skills laboratory at FRPMC. Methods: It was a descriptive cross-sectional study conducted at FRPMC, with 100 medical students of the first-year M.B.B.S. participating in this study. During the academic year, study participants attended six modules from Feb to Dec 2020. A 5-point Likert scale questionnaire was given to each student at the end of the module. Data has been reported as Mean ± SD and an internal consistency evaluation form for each variable was calculated. Results: A total of 100 students participated in the study, 90% of the study participants agreed that their overall skill lab experience of training in the skills lab was satisfactory. 68% of respondents felt that instructors play an important role in organizing and structuring the course material, and 58 to 61% said that the clinical skills training simulates a hospital setting where practicing on real patients would take place. The pre-reading materials and instructions should be provided in advance, according to 64% of students. Conclusion Students’ perceptions and experiences of teaching clinical skills were generally positive. Students showed overall satisfaction over the motivational conducive environment, content taught, gaining confidence, learning procedural skills, and adequacy of the educational equipment for learners in the newly established CSL at FRPMC.

Retrospective cohort study to evaluate the efficacy and safety of T-DXd in HER2-positive unresectable advanced or recurrent gastric or gastroesophageal junction cancer: EN-DEAVOR study.

274 Background: Relatively limited clinical data exists for patients who receive T-DXd treatment in the 3L+ setting for HER2-positive unresectable advanced/recurrent gastric or gastroesophageal junction cancer. Insufficient data exists on real world patient populations that are likely to be excluded from clinical trials (e.g., elderly patients, those with significant co-morbidities). Therefore, this study was designed to evaluate the real-world clinical effectiveness and safety of T-DXd in patients with HER2-positive gastric cancer. Methods: This study (UMIN000049032) was a real-world, multicenter, non-interventional, retrospective cohort study conducted in Japan. Patients with HER2-positive unresectable advanced/recurrent gastric or gastroesophageal junction cancer treated with T-DXd were enrolled. The patients were first administered T-DXd between Sep 2020, when T-DXd became available for clinical use in Japan, and Sep 2021. We followed up on these patients' records until Sep 2022. The endpoints were overall survival (OS), real-world progression-free survival (rwPFS), time to treatment failure (TTF), objective response (ORR), adverse events (AEs) of Grade ≥3, and adverse events leading to dose reduction, interruption, or discontinuation. Results: A total of 318 patients were enrolled, and 312 patients were analyzed, excluding 6 patients due to double enrollment or miss-enrollment (median age 70 years, performance status 0 or 1/≥2 [87%/12%], 43% patients with ascites). Median OS, rwPFS, and TTF were 8.90 months, 4.57 months, and 3.94 months, respectively. Overall ORR was 32.7%; it was 43.8% in the 226 patients with a target lesion. Regarding safety, the incidence of Grade ≥ 3 AEs was 48.4%. A total of 190/312 (60.9%) patients treated with T-DXd experienced dose reduction, interruption, or discontinuation of T-DXd treatment due to AEs. The most common reasons for dose reduction or interruption were neutrophil count decreased, anorexia, and malaise, while the most common reasons for discontinuation were interstitial pneumonia, anorexia, malaise, and nausea. Conclusions: This study demonstrates the effectiveness and safety of T-DXd in the 3L+ setting in real-world patients with HER2-positive gastric or gastroesophageal cancer. No new safety signals were identified on the basis of this study.

Development and validation of a novel home-made bench-top training model for retrograde intrarenal surgery.

To develop and validate a low-cost homemade bench-top training model to facilitate retrograde intrarenal surgery (RIRS) training. The RIRS training model (G-Model) was developed using a surgical glove and a recycled ureter access sheath. Fifteen participants including 10 residents and 5 urologists were enrolled. Designed training curriculum for residents was carried out. Face validity, content validity, construct validity and criterion validity evaluation of the G-Model were carried out. The global score of face and content validity was 4.15 ± 0.53 and 4.65 ± 0.29, respectively. For construct validity, the overall modified global rating scale (mGRS) score was significantly improved [12.5 (5.25) vs. 24.0 (5.25), p = 0.004], and the total task time was significantly shortened (39.5 ± 4.48min vs. 24.1 ± 3.81min, p < 0.001) within residents after G-Model training. The baseline mGRS score and total task time of residents were poorer than those of urologists [12.5 (5.25) vs. 32.0 (1.00), p < 0.001; 39.5 ± 4.48min vs. 16.0 ± 1.58min, p < 0.001]. Spearman correlation analysis revealed strong correlations between residents' G-Model and real patient performance. The current study presented a valid low-cost easily accessible RIRS bench-top training model which could facilitate skill acquisition and translate to real-life scenario.

A Comprehensive Review on Virtual Articulators.

The virtual articulator (VA) is a technology that simulates the jaw relation in a computer-generated setting. Augmented and virtual reality have been utilized as digital technology, which aids in many areas of dentistry and dental education. Today, a practicing dentist must keep up with the newer technologies, but with technology evolving so quickly it becomes challenging. In dentistry, the possibilities for digitization and technological advancements are limitless.Virtual articulators (VAs) allow a complete occlusion analysis using dental models that replicate all mandibular motions in static and dynamic scenarios. VAwhen executed in addition to other software enhances treatment planning and patient education, allowing quicker and more precise individualized diagnoses. The main objective of this study was to describe and evaluate the study outcomes in the available research on VAs, assess their needs, and evaluate their advantages and limitations in various aspects. A PubMed Centralsearch was made of dental journals, with the identification of 135 articlesout of which 30 were finally selected. The investigations conducted to evaluate the VA's dependability provide good visualization of the quantity and location of the dynamic interactions. A precise instrument for fully analyzing occlusion in a real patient is the virtual articulator.

A Generative Pretrained Transformer (GPT)-Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study.

Communication is a core competency of medical professionals and of utmost importance for patient safety. Although medical curricula emphasize communication training, traditional formats, such as real or simulated patient interactions, can present psychological stress and are limited in repetition. The recent emergence of large language models (LLMs), such as generative pretrained transformer (GPT), offers an opportunity to overcome these restrictions. The aim of this study was to explore the feasibility of a GPT-driven chatbot to practice history taking, one of the core competencies of communication. We developed an interactive chatbot interface using GPT-3.5 and a specific prompt including a chatbot-optimized illness script and a behavioral component. Following a mixed methods approach, we invited medical students to voluntarily practice history taking. To determine whether GPT provides suitable answers as a simulated patient, the conversations were recorded and analyzed using quantitative and qualitative approaches. We analyzed the extent to which the questions and answers aligned with the provided script, as well as the medical plausibility of the answers. Finally, the students filled out the Chatbot Usability Questionnaire (CUQ). A total of 28 students practiced with our chatbot (mean age 23.4, SD 2.9 years). We recorded a total of 826 question-answer pairs (QAPs), with a median of 27.5 QAPs per conversation and 94.7% (n=782) pertaining to history taking. When questions were explicitly covered by the script (n=502, 60.3%), the GPT-provided answers were mostly based on explicit script information (n=471, 94.4%). For questions not covered by the script (n=195, 23.4%), the GPT answers used 56.4% (n=110) fictitious information. Regarding plausibility, 842 (97.9%) of 860 QAPs were rated as plausible. Of the 14 (2.1%) implausible answers, GPT provided answers rated as socially desirable, leaving role identity, ignoring script information, illogical reasoning, and calculation error. Despite these results, the CUQ revealed an overall positive user experience (77/100 points). Our data showed that LLMs, such as GPT, can provide a simulated patient experience and yield a good user experience and a majority of plausible answers. Our analysis revealed that GPT-provided answers use either explicit script information or are based on available information, which can be understood as abductive reasoning. Although rare, the GPT-based chatbot provides implausible information in some instances, with the major tendency being socially desirable instead of medically plausible information.

Reducing windmill artifacts in clinical spiral CT using a deep learning-based projection raw data upsampling: Method and robustness evaluation.

Multislice spiral computed tomography (MSCT) requires an interpolation between adjacent detector rows during backprojection. Not satisfying the Nyquist sampling condition along the z-axis results in aliasing effects, also known as windmill artifacts. These image distortions are characterized by bright streaks diverging from high contraststructures. The z-flying focal spot (zFFS) is a well-established hardware-based solution that aims to double the sampling rate in longitudinal direction and therefore reduce aliasing artifacts. However, given the technical complexity of the zFFS, this work proposes a deep learning-based approach as an alternativesolution. We propose a supervised learning approach to perform a mapping between input projections and the corresponding rows required for double sampling in the z-direction. We present a comprehensive evaluation using both a clinical dataset obtained using raw data from 40 real patient scans acquired with zFFS and a synthetic dataset consisting of 100 simulated spiral scans using a phantom specifically designed for our problem. For the clinical dataset, we utilized 32 scans as training set and 8 scans as validation set, whereas for the synthetic dataset, we used 80 scans for training and 20 scans for validation purposes. Both qualitative and quantitative assessments are conducted on a test set consisting of nine real patient scans and six phantom measurements to validate the performance of our approach. A simulation study was performed to investigate the robustness against different scan configurations in terms of detector collimation and pitchvalue. In the quantitative comparison based on clinical patient scans from the test set, all network configurations show an improvement in the root mean square error (RMSE) of approximately 20% compared to neglecting the doubled longitudinal sampling by the zFFS. The results of the qualitative analysis indicate that both clinical and synthetic training data can reduce windmill artifacts through the application of a correspondingly trained network. Together with the qualitative results from the test set phantom measurements it is emphasized that a training of our method with synthetic data resulted in superior performance in windmill artifactreduction. Deep learning-based raw data interpolation has the potential to enhance the sampling in z-direction and thus minimize aliasing effects, as it is the case with the zFFS. Especially a training with synthetic data showed promising results. While it may not outperform zFFS, our method represents a beneficial solution for CT scanners lacking the necessary hardware components forzFFS.

Soybean Oil-Based 3D Printed Mesh Designed for Guided Bone Regeneration (GBR) in Oral Surgery.

This study aims to obtain a cyto-compatible 3D printable bio-resin for the manufacturing of meshes designed from acquired real patients' bone defect to be used in future for guided bone regeneration (GBR), achieving the goal of personalized medicine, decreasing surgical, recovery time, and patient discomfort. To this purpose, a biobased, biocompatible, and photo-curable resin made of acrylated epoxidized soybean oil (AESO) diluted with soybean oil (SO) is developed and 3D printed using a commercial digital light processing (DLP) 3D printer. 3D printed samples show good thermal properties, allowing for thermally-based sterilization process and mechanical properties typical of crosslinked natural oils (i.e., E = 12MPa, UTS = 1.5MPa), suitable for the GBR application in the oral surgery. The AESO-SO bio-resin proves to be cytocompatible, allowing for fibroblast cells proliferation (viability at 72h > 97%), without inducing severe inflammatory response when co-cultured with macrophages, as demonstrated by cytokine antibody arrays, that is anyway resolved in the first 24 h. Moreover, accelerated degradation tests prove that the bio-resin is biodegradable in hydrolytic environments.

Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke

BackgroundArtificial intelligence (AI) has become a promising tool in medicine. ChatGPT, a large language model AI Chatbot, shows promise in supporting clinical practice. We assess the potential of ChatGPT as...

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency.

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Salt-assisted liquid–liquid microextraction for determination of haloperidol in human plasma by LC-MS/MS

Haloperidol is an antipsychotic used in the treatment of schizophrenia. Compared to other antipsychotics, it is widely used in developing countries due to its affordable price. Haloperidol has a narrow therapeutic range and variable pharmacokinetics; therefore, therapeutic drug monitoring (TDM) is recommended. For this reason, in this study, an easily applicable, fast, selective, accurate, reliable, and economical LC-MS/MS method was developed for the determination of haloperidol in human plasma for use in TDM and also method was validated according to European Medicines Agency (EMA) Bioanalytical method validation guidelines. In the developed method, analyte and internal standard were extracted from plasma by salt-assisted liquid-liquid microextraction (SALLME) technique and after that injected to the LC system. The limit of quantification of haloperidol was determined as 1 ng/ml. The calibration curve was validated between 1-15 ng/ml, with correlation coefficients &gt;0.99. In addition, the developed method was used to determine drug concentration levels in the plasma of real patients.

Deep learning-based projection synthesis for low-dose cone-beam computed tomography imaging in image-guided radiotherapy.

The imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed. Included in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions. The phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains. The proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.

Construction of a laparoscopic appendectomy model.

Appendectomy is the standard treatment for appendicitis, with the laparoscopic technique offering benefits like lower infection rates and quicker recovery. However, residents often have their first practical experience with the procedure on real patients, increasing surgical risks. In this context, medical simulation emerges as a crucial methodology, allowing professionals to experience a variety of scenarios while preventing harm to patients. The objective of this study is to describe the production of an "ex-vivo" simulation model for laparoscopic appendectomy. Cold ceramic structures were used to manually shape the anatomical model of the appendix, ensuring its rigidity. On this model, we poured materials to create a flexible mold using acetic silicone. Once the mold was made, we filled it with thermo-moldable styrene polymer rubber, along with dye, and fused it at a specific temperature. This process resulted in the manufacture of a piece that simulates the appendix, being tear-resistant and suturable, faithfully replicating the structure and characteristics of a human organ. The low weight of the materials facilitates transport, allowing them to be reproduced and used in various situations, from training in hospital settings to universities. The model is applicable in didactic simulations with medical students, residents, and surgeons. Its ease of production and low cost contribute to the practices being repeatable, ensuring a better development of surgical skills. This work not only contributes to the advancement of medical simulation but also highlights the importance of innovative and collaborative solutions in improving medical education and promoting patient safety.

Efficiency of convolutional neural networks of different architecture for the task of depression diagnosis from EEG data

The purpose of this paper is to comparatively analyse the efficiency of using artificial neural networks with different convolutional and recurrent architectures in the task of depression diagnosis based on electroencephalogram (EEG) data. Open datasets were chosen as objects of the study and own EEG data of real patients with depression were collected. Methods. To solve the problem of identifying biomarkers of depressive disorder from EEG data, we used convolutional neural networks using two-dimensional or one-dimensional convolution operation, as well as hybrid models of convolutional and recurrent neural networks. To test the developed models of artificial neural networks, we selected open data sets, performed an experiment to collect our own data from real depressed patients, and merged the prepared data sets. The result of this work is analysis and comparison of the performance of different classifiers based on convolutional and recurrent neural network models. Conclusion. We show that the average accuracy of classification of depressive disorder in a test sample using cross-validation was 0.68. The results are consistent with the known results from the literature for small patient-disaggregated datasets. Although the accuracy obtained in this task is insufficient for practical application of the model, it can be argued that further research to improve the efficiency of the model is promising, as well as the need to create a sufficiently large representative dataset of depressed patients, which is an important scientific task for further construction of biophysical models of depressive disorders.

Use of clinical simulators and development of professional skills in dental students: systematic review

With the advancement of new technologies and teaching methods, dental schools face significant challenges in reorganizing their educational processes. This requires the incorporation of pedagogical systems that enhance the intellectual abilities of students, allowing them to respond effectively to the demands of society. The objective of this review was to analyze the impact of the use of clinical simulators on the development of professional skills in dental students. A systematic review was carried out using the Scielo, Lilacs and Redalyc databases. The search focused on studies related to the variables: simulators, learning, skills, students and dentistry. From the analysis of the content of the selected articles, three main themes were identified: the importance of the use of dental simulators, the development of skills in simulated environments, and the types of clinical simulators used in the training of dental students. The use of simulators in dental training is essential to improve clinical practice safely. These devices allow students to fine-tune their motor skills and develop a high level of confidence, which is crucial for performing procedures on real patients in their future professional practice

APTAnet: an atom-level peptide-TCR interaction affinity prediction model.

The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.