Actual Clinical Application Research Articles

Weighted Dual-Channel Model for Arrhythmia Detection using Single Electrocardiogram Signal

The single-lead method plays a central role in arrhythmia warning, and its anomaly detection is of practical significance for the prevention of heart diseases. However, the detection performance is often limited due to the limited information content of single-lead electrocardiogram (ECG) signals. To address this issue, this paper proposes a weighted dualchannel arrhythmia classification model aimed at improving the detection performance of single-lead ECG signals. The model employs a parallel structure of CNN-LSTM and Transformer to capture both local and global features of the ECG signals. By applying weighted learning to the features from the two channels, the model achieves precise arrhythmia classification. Additionally, to alleviate the class imbalance problem, a weighted cross-entropy loss function is introduced, further enhancing the classification performance. Experimental results based on the MIT-BIH arrhythmia database demonstrate that the proposed model exhibits excellent performance in terms of overall accuracy, F1 score, and sensitivity, achieving 99.33%, 94.71%, and 99.33%, respectively. Compared with existing classification models, the proposed model shows superior classification performance and significant practical value, providing solid technical support for the auxiliary diagnosis of arrhythmias and bringing new possibilities for actual clinical applications.

An in vitro study of coagulation evaluation in obstetric hemorrhage for pregnancy-induced hypertension with coagulation and platelet function analyzer

ABSTRACT This study aimed to establish in vitro hemodilution and resupplementation assays for obstetric hemorrhage in pregnancy-induced hypertension (PIH) and to monitor the coagulation function dynamically using a coagulation and platelet function analyzer. Forty-seven singleton pregnant women were divided into normal (n = 24) and PIH (n = 23) groups. Peripheral blood samples were used to construct the assays, and the activated clotting time (ACT), clotting rate (CR), and platelet function index (PF) were measured. The results showed that the baseline ACT was higher in the PIH group (p < 0.01). Hemodilution assays showed decreased ACT and increased CR and PF, with ACT changes significantly lower in the PIH group (p < 0.05). CR changed most in both groups at lower dilution ratios (35% to 50%), while ACT changed most at a higher dilution ratio (75%). In the resupplementation assay, ACT exhibited the most significant response. The analyzer effectively detected differences between pregnant women with and without PIH. Thus, we need to pay more attention to the changes of ACT in the actual clinical application to assess the coagulation status of parturients.

Identification of dental implant systems from low-quality and distorted dental radiographs using AI trained on a large multi-center dataset

Most artificial intelligence (AI) studies have attempted to identify dental implant systems (DISs) while excluding low-quality and distorted dental radiographs, limiting their actual clinical use. This study aimed to evaluate the effectiveness of an AI model, trained on a large and multi-center dataset, in identifying different types of DIS in low-quality and distorted dental radiographs. Based on the fine-tuned pre-trained ResNet-50 algorithm, 156,965 panoramic and periapical radiological images were used as training and validation datasets, and 530 low-quality and distorted images of four types (including those not perpendicular to the axis of the fixture, radiation overexposure, cut off the apex of the fixture, and containing foreign bodies) were used as test datasets. Moreover, the accuracy performance of low-quality and distorted DIS classification was compared using AI and five periodontists. Based on a test dataset, the performance evaluation of the AI model achieved accuracy, precision, recall, and F1 score metrics of 95.05%, 95.91%, 92.49%, and 94.17%, respectively. However, five periodontists performed the classification of nine types of DISs based on four different types of low-quality and distorted radiographs, achieving a mean overall accuracy of 37.2 ± 29.0%. Within the limitations of this study, AI demonstrated superior accuracy in identifying DIS from low-quality or distorted radiographs, outperforming dental professionals in classification tasks. However, for actual clinical application of AI, extensive standardization research on low-quality and distorted radiographic images is essential.

Virtual Reality Surgical Simulations Using Fusion Three-Dimensional Images

In this section, we defined virtual reality(VR)surgical simulations using fusion three-dimensional(3D)images, which are 3D images created by fusing multiple medical image data. The more detailed the fusion of 3D images, the more knowledge and effort are required. In addition, 3D fusion images vary greatly with each case and depend on the skill and orientation of the image creator and the image processing software used. Some creators produce a fused 3D image with ample details to simulate tissue deformation, whereas others are limited to rough observations and use two-dimensional cross-sectional images for detailed anatomical information. Thus, there is no gold standard for creating fused 3D images or VR surgical simulations. Therefore, it is important to clarify the objective of a VR surgical simulation. An understanding of image-processing technology is useful in terms of software selection and image-processing efficiency. This section outlines the construction of fused 3D images and the use of VR surgical simulations based on actual clinical applications.

Numerical investigation of the heat flux frequency effect on the doxorubicin absorption by Bio MOF11 carrier: A molecular dynamics approach

The present study investigated the effect of heat flux frequency on doxorubicin adsorption by bio MOF11 biocarrier using molecular dynamics simulation. This simulation examined the effect of several heat flux frequencies (0.001, 0.002, 0.005, and 0.010 1/fs) on the quantity of drug particles absorbed, mean square displacement (MSD), diffusion coefficient, and interaction energy. The present outputs of simulations predicted the structural stability of the modeled MOF-drug system in 300 K. Also, simulation outputs predicted by frequency optimization, the adsorption of target drug inside MOF11 maximized, and efficiency of this sample in actual clinical applications, such as drug delivery process increased. Numerically, the optimum value of frequency was estimated to be 0.005 1/fs. Using this heat setting, the interaction energy between MOF 11 and the doxorubicin drug increased to −929.05 kcal/mol, and the number of penetrated drug particles inside MOF11 converged to 207 atoms. The results reveal that the MSD parameter reached 64.82 Å2 after 100000-time steps. By increasing frequency to 0.005 fs−1, this increased to 78.05 Å2. By increasing MSD parameter, the drug diffusion process effectively occurred, and the diffusion coefficient increased from 67.29 to 82.47 nm2/ns. It is expected that the findings of present investigation guide the design of more efficient drug delivery platforms, enhance drug-carrier interactions, improve manufacturing processes, and aid in developing novel nanomaterials with enhanced adsorption properties for various applications.

Clinical Application of Pediatric Sleep Endoscopy: An International Survey.

To investigate through an international survey the actual clinical application of drug-induced sleep endoscopy (DISE) in pediatric patients with obstructive sleep apnea (OSA) and to clarify the use, application, clinical indications, and protocol of pediatric DISE. A specific survey about pediatric DISE was initially developed by five international otolaryngologists with expertise in pediatric sleep apnea and drug-induced sleep endoscopy and was later spread to experts in the field of sleep apnea, members of different OSA-related associations. A total of 101 participants who answered all the survey questions were considered in the study. Sixty-four sleep apnea experts, equivalent to 63.4% of interviewed experts, declared they would perform DISE in pediatric OSA patients. A total of 81.9% of responders agreed to consider the DISE as the first diagnostic step in children with persistent OSA after adenotonsillectomy surgery, whereas 55.4% disagreed with performing DISE at the same time of scheduled adenotonsillectomy surgery to identify other possible sites of collapse. In the case of young patients with residual OSA and only pharyngeal collapse during DISE, 51.8% of experts agreed with performing a velopharyngeal surgery. In this case, 27.7% disagreed and 21.4% were neutral. Pediatric DISE is internationally considered to be a safe and effective procedure for identifying sites of obstruction and collapse after adenotonsillectomy in children with residual OSA. This is also useful in cases of patients with craniofacial malformations, small tonsils, laryngomalacia or Down syndrome to identify the actual site(s) of collapse. Despite this evidence, our survey highlighted that pediatric DISE is not used in different sleep centers.

Deep learning to assess bone quality from panoramic radiographs: the feasibility of clinical application through comparison with an implant surgeon and cone-beam computed tomography.

Bone quality is one of the most important clinical factors for the primary stability and successful osseointegration of dental implants. This preliminary pilot study aimed to evaluate the clinical applicability of deep learning (DL) for assessing bone quality using panoramic (PA) radiographs compared with an implant surgeon's subjective tactile sense and cone-beam computed tomography (CBCT) values. In total, PA images of 2,270 edentulous sites for implant placement were selected, and the corresponding CBCT relative gray value measurements and bone quality classification were performed using 3-dimensional dental image analysis software. Based on the pre-trained and fine-tuned ResNet-50 architecture, the bone quality classification of PA images was classified into 4 levels, from D1 to D4, and Spearman correlation analyses were performed with the implant surgeon's tactile sense and CBCT values. The classification accuracy of DL was evaluated using a test dataset comprising 454 cropped PA images, and it achieved an area under the receiving characteristic curve of 0.762 (95% confidence interval [CI], 0.714-0.810). Spearman correlation analysis of bone quality showed significant positive correlations with the CBCT classification (r=0.702; 95% CI, 0.651-0.747; P<0.001) and the surgeon's tactile sense (r=0.658; 95% CI, 0.600-0.708, P<0.001) versus the DL classification. DL classification using PA images showed a significant and consistent correlation with CBCT classification and the surgeon's tactile sense in classifying the bone quality at the implant placement site. Further research based on high-quality quantitative datasets is essential to increase the reliability and validity of this method for actual clinical applications.

IL-17 and IL-10 as potential biomarkers for diagnosing periodontitis patients: A Review

Background:Evaluation methods for periodontal disorders are continually being refined to yield more precise, reliable, and time-efficient findings. Radiographic reports and clinical assessments of periodontal tissues, which served as the basis for earlier trends, were used to identify disorders affecting the tooth-supporting structures. As traditional approaches have become more apparent gradually over time, new methods emerged, some of which have been empirically validated. One example is the detection and measurement of biomarkers in bodily fluids. One of the most pressing issues in periodontal field nowadays is the search for an idealized periodontal diagnostic/prognostic biomarker that can both identify diseased areas and track the therapeutic success of periodontal treatment. Both IL-17 and IL-10 have shown connection to periodontitis that could encourage using them as diagnostic tools.&#x0D; Objective: To provide a review of the role and association of the above biomarkers to periodontitis.&#x0D; Patients and Methods: A systematic research of Google scholar, Scopus, PubMed Central, research gate was conducted using keywords such as diagnosis, periodontitis, IL-17, IL-10, biomarkers, Saliva and gingival crevicular fluid (GCF).&#x0D; Results: out of 58 records which resulted from the initial search only 14 were included which used roles, associations, and diagnosis as their outcomes.&#x0D; Conclusion: Evidence from the selected records suggests that the above stated biomarkers might be able to discriminate periodontitis from health, monitoring disease progression and post treatment outcome, but actual clinical application needs much further research.

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model.

The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.

Expert consensus on clinical drug prevention and treatment of osteonecrosis of the femoral head(2022)

With the in-depth understanding of osteonecrosis of femoral head (ONFH), and more and more patients seeking medical treatment in the early stage of the disease, surgical treatment of femoral head necrosis alone is no longer sufficient for the current treatment of patients' demand, how to rationally and effectively apply drugs to strengthen the early prevention and treatment of femoral head necrosis and delay the progression of disease is becoming more and more important. This article combines the latest expert consensus and evidence-based medical evidence on the principles of ONFH diagnosis and treatment in Chinese and Western medicine at home and abroad, combined with domestic actual clinical application experience, and is organized by experts from Association Related to Circulation Osseous Chinese Microcirculation Society (CSM-ARCO) to write this consensus, focusing on the types of ONFH drugs, the characteristics, safety, rationality and basic principles of drug use provide reference opinions for the safe, reasonable, standardized and effective drug use of medical institutions at all levels. This consensus is only an expert guideline based on literature and clinical experience, not as a requirement for mandatory implementation, let alone as a legal basis. The clinical practice could be tailored to the actual local conditions to develop appropriate prevention and treatment measures for patients.

Advances in optical molecular imaging for neural visualization.

Iatrogenic nerve injury is a significant complication in surgery, which can negatively impact patients' quality of life. Currently, the main clinical neuroimaging methods, such as computed tomography, magnetic resonance imaging, and high-resolution ultrasonography, do not offer precise real-time positioning images for doctors during surgery. The clinical application of optical molecular imaging technology has led to the emergence of new concepts such as optical molecular imaging surgery, targeted surgery, and molecular-guided surgery. These advancements have made it possible to directly visualize surgical target areas, thereby providing a novel method for real-time identification of nerves during surgery planning. Unlike traditional white light imaging, optical molecular imaging technology enables precise positioning and identifies the cation of intraoperative nerves through the presentation of color images. Although a large number of experiments and data support its development, there are few reports on its actual clinical application. This paper summarizes the research results of optical molecular imaging technology and its ability to realize neural visualization. Additionally, it discusses the challenges neural visualization recognition faces and future development opportunities.

Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) is a promising imaging modality, which has played a key role in disease progression and treatment response. However, the quality of FMT reconstruction is limited by the strong scattering and inadequate surface measurements, which makes it a highly ill-posed problem. Improving the quality of FMT reconstruction is crucial to meet the actual clinical application requirements. We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction. The proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a support set using a neighbor expansion strategy based on the orthogonal least squares algorithm. The performance of the algorithm was tested through numerical simulations, physical phantom experiments, and small animal experiments. The results of the experiments demonstrated that the NASOLS significantly improves the reconstruction of images according to indicators, especially for double-target reconstruction. NASOLS can recover the fluorescence target with a good location error according to simulation experiments, phantom experiments and small mice experiments. This method is suitable for sparsity target reconstruction, and it would be applied to early detection of tumors.

P-107 MICROFLUIDIC SPERM SEXING: An XLD (X-linked Disorder) model for preventing genetic disorders in newborns

Abstract Study question Can sperm sexing based on unique surface membrane markers – enable genotypic sperm sorting by allosomal content to prevent certain X-linked disorders in newborns? Summary answer In an X-Linked-Disorder prevention model, we utilized a microfluidic device functionalized with H-Y monoclonal antibodies attached to magnetic beads to enrich Y-chromosome bearing sperm sorts. What is known already About 4% of newborns are at risk of being born with an X-linked disease such as Fragile X Syndrome. The current clinically approved mitigation plan for preventing such genetic disorders is to consider Preimplantation Genetic Testing for Monogenetic diseases (PGT-M) which involves an invasive microsurgical procedure on fragile embryos. To minimize this invasiveness, we proposed a less invasive microsurgical exclusion approach to prevent such genetic disorders by genotypically sorting sperm cells that can be used for IUI/IVF/ICSI – with microfluidics – using their allosomal characteristics. Study design, size, duration Semen samples purchased from FairFax Cryobank from anonymous donors were used in immuno-magnetic microfluidic sorting protocols with the use of phycoerythrin (PE) binding Tetrameric Antibody Complexes (TAC) coupled with magnetic beads to tag H-Y monoclonal antibody (mab) binding activities on the sperm surface membrane. Participants/materials, setting, methods Immuno-magnetic sorting was conducted with specially fabricated microfluidic-sperm-sorting (MSS) chips. Sort yield and purities of positively selected sperm populations were assessed by flow-cytometry, immunofluorescence, fluorescent-in-situ-hybridization (FISH) and by quantitative-PCR (qPCR – Cq &amp; Tm). Quantitative experiments were run in triplicates and the results were expressed as absolute means for comparison between paired variables. Statistical analyses were performed using Student’s t-test and a p-value of less than 0.05 was considered to be statistically significant. Main results and the role of chance For the assessment of H-Y sperm sorts, our preliminary data showed that flow cytometry was able to confirm the positive sperm selection (which were mostly Y-chromosome bearing sperm cells) in a sperm genotyping protocol – initially utilizing immuno-magnetic sort tubes prior to flowing through microfluidic chips. Both immuno-magnetic sort tubes and the microfluidic chips were able to optimally confirm from qPCR-Cq means – the negative selection – being the X-chromosome bearing sperm that may have been largely unselected by the H-Y-mab. Values for Cq means compared under H-Y-qPCR data sets were statistically significant with a p value of 0.000023 (p values is significant at &amp;lt; 0.05). Our study was able to elucidate a potential less invasive clinical application of genotypically sorting sperm cells with mab-functionalized microfluidic chips for an XLD-model. Molecular down stream applications like flow cytometry may therefore be utilized to experimentally confirm sort purities of positively selected sperm populations (with H-Y markers) – based on the utility of the SMCX and SMCY gene in the X and Y sperm chromosome bearing sperm cells respectively. Limitations, reasons for caution Physiological effect of magnetic fields of varying tesla or gauss strengths on sperm selected with immuno-magnetic beads may warrant further safety studies prior to actual clinical applications. Optimal selection results may be achieved with the use of freshly collected normospermic rather than frozen-thawed samples. Wider implications of the findings Although we had de-emphasized the clinical application of the microfluidic sperm sexing protocol for legitimate use in sex selection and gender balancing, this study design was solely focused on adapting a less invasive method of preventing X-linked genetic disorders with the potential exclusion of microsurgical procedures leading to PGT-M. Trial registration number NA

Development of Phytochemical Delivery Systems by Nano-Suspension and Nano-Emulsion Techniques.

The awareness of the existence of plant bioactive compounds, namely, phytochemicals (PHYs), with health properties is progressively expanding. Therefore, their massive introduction in the normal diet and in food supplements and their use as natural therapeutics to treat several diseases are increasingly emphasized by several sectors. In particular, most PHYs possessing antifungal, antiviral, anti-inflammatory, antibacterial, antiulcer, anti-cholesterol, hypoglycemic, immunomodulatory, and antioxidant properties have been isolated from plants. Additionally, their secondary modification with new functionalities to further improve their intrinsic beneficial effects has been extensively investigated. Unfortunately, although the idea of exploiting PHYs as therapeutics is amazing, its realization is far from simple, and the possibility of employing them as efficient clinically administrable drugs is almost utopic. Most PHYs are insoluble in water, and, especially when introduced orally, they hardly manage to pass through physiological barriers and scarcely reach the site of action in therapeutic concentrations. Their degradation by enzymatic and microbial digestion, as well as their rapid metabolism and excretion, strongly limits their in vivo activity. To overcome these drawbacks, several nanotechnological approaches have been used, and many nanosized PHY-loaded delivery systems have been developed. This paper, by reporting various case studies, reviews the foremost nanosuspension- and nanoemulsion-based techniques developed for formulating the most relevant PHYs into more bioavailable nanoparticles (NPs) that are suitable or promising for clinical application, mainly by oral administration. In addition, the acute and chronic toxic effects due to exposure to NPs reported so far, the possible nanotoxicity that could result from their massive employment, and ongoing actions to improve knowledge in this field are discussed. The state of the art concerning the actual clinical application of both PHYs and the nanotechnologically engineered PHYs is also reviewed.

A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data

BackgroundIrreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients.MethodsPatients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method.ResultsWe enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA.ConclusionWe constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI.

Accuracy of immediate dental implant placement with task-autonomous robotic system and navigation system: An in vitro study.

The aim of this study was to compare the accuracy of dental implant placement in a single tooth gap, including the postextraction site and healed site, using a task-autonomous robotic system and a dynamic navigation system. Forty partially edentulous models requiring both immediate and conventional implant placement were randomly divided into a robotic system group and a navigation system group. The coronal, apical, and angular deviations of the implants were measured and assessed between the groups. The deviations in immediate implant placement were compared between the robotic system and dynamic navigation system groups, showing a mean (±SD) coronal deviation of 0.86 ± 0.36 versus 0.70 ± 0.21 mm (p = .101), a mean apical deviation of 0.77 ± 0.34 versus 0.95 ± 0.38 mm (p = .127), and a mean angular deviation of 1.94 ± 0.66° versus 3.44 ± 1.38° (p < .001). At the healed site, significantly smaller coronal deviation (0.46 ± 0.29 vs. 0.70 ± 0.30 mm, p = .005), apical deviation (0.56 ± 0.30 vs. 0.85 ± 0.25 mm, p < .001), and angular deviation (1.36 ± 0.54 vs. 1.80 ± 0.70 mm, p = .034) were found in the robotic system group than in the dynamic navigation group. The position in both immediate and conventional implant placement was more precise with the task-autonomous robotic system than with the dynamic navigation system. Its performance in actual clinical applications should be confirmed in further trials.

MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data

The existing methods for accurate brain tumor (BT) segmentation based on homogeneous datasets show significant performance degradation in actual clinical applications and lacked heterogeneous data analysis. To address these issues, we designed a deep learning-based multiscale dilated features up-sampling network (MDFU-Net) for accurate BT segmentation from heterogeneous brain data. Our method primarily uses the strength of multiscale dilated features (MDF) inside the encoder module to improve the segmentation performance. For the final segmentation, a simple yet effective decoder module is designed to process the dense spatial MDF. For experiments, our MDFU-Net is trained on one dataset and tested with another dataset in a heterogeneous environment, showing quantitative results of the Dice similarity coefficient (DC) of 62.66%, intersection over union (IoU) of 56.96%, specificity (Spe) of 99.29%, and sensitivity (Sen) of 51.98%, which were higher than those of the state-of-the-art methods. There are several reasons for the lower values of the evaluation metrics of the heterogeneous dataset, including the change in characteristics of different MRI modalities, the presence of minor lesions, and a highly imbalanced dataset. Moreover, the experimental results for a homogeneous dataset showed that our MDFU-Net achieved a DC of 82.96%, IoU of 74.94%, Spe of 99.89%, and Sen of 68.05%, which were also higher than those of the state-of-the-art methods. Our system, which is based on heterogeneous brain data as well as homogeneous brain data, can be advantageous to radiologists and medical experts.

Machine Learning Models to Forecast Outcomes of Pituitary Surgery: A Systematic Review in Quality of Reporting and Current Evidence.

The complex nature and heterogeneity involving pituitary surgery results have increased interest in machine learning (ML) applications for prediction of outcomes over the last decade. This study aims to systematically review the characteristics of ML models involving pituitary surgery outcome prediction and assess their reporting quality. We searched the PubMed, Scopus, and Web of Knowledge databases for publications on the use of ML to predict pituitary surgery outcomes. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to assess report quality. Our search strategy was based on the terms "artificial intelligence", "machine learning", and "pituitary". 20 studies were included in this review. The principal models reported in each article were post-surgical endocrine outcomes (n = 10), tumor management (n = 3), and intra- and postoperative complications (n = 7). Overall, the included studies adhered to a median of 65% (IQR = 60-72%) of TRIPOD criteria, ranging from 43% to 83%. The median reported AUC was 0.84 (IQR = 0.80-0.91). The most popular algorithms were support vector machine (n = 5) and random forest (n = 5). Only two studies reported external validation and adherence to any reporting guideline. Calibration methods were not reported in 15 studies. No model achieved the phase of actual clinical applicability. Applications of ML in the prediction of pituitary outcomes are still nascent, as evidenced by the lack of any model validated for clinical practice. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to enable their use in clinical practice. Further adherence to reporting guidelines can help increase AI's real-world utility and improve clinical practice.

Real-world applications of PARPi maintenance therapy for recurrent ovarian cancer: A single-center study in China

ObjectiveTo assess the actual clinical application of poly (adenosine diphosphate-ribose) polymerase inhibitor (PARPi) maintenance therapy in Chinese patients with recurrent ovarian cancer, and to explore prognostic factors associated with progression-free survival (PFS). MethodsWe retrospectively assessed real-world clinical data from our hospital using the inclusion and exclusion criteria of representative randomized controlled trials, analyzed the prognosis, and performed univariate and multivariate analyses of prognostic factors. ResultsBetween 2019 and 2022, the proportion of platinum-sensitive recurrence ovarian cancer patients who received PARPi maintenance therapy increased to 29.6%, 53.3%, 43.8% and 62.2%, respectively, each year. A total of 48 patients were included in the prognostic analysis, of which 32 and 16 received olaparib and niraparib, respectively. Using the criteria of the Study19 and SOLO2 studies, the olaparib group in our patients had coincidence rates of 56.3% and 18.8%, respectively. Using the criteria of the NOVA and NORA studies, the niraparib group had coincidence rates of 31.3% and 37.5%, respectively. Median PFS was 26.1 months (95% CI 20.2–32.1). Response to primary therapy was an independent prognostic factor for PFS (relative risk, 3.248; 95% CI 1.081–9.757, P = 0.036). ConclusionsPARPi maintenance therapy was also effective in real world applications. Complete response (CR) to primary therapy was an independent factor favorably affecting PFS. Therefore, primary treatment choices aimed at optimal cytoreduction during primary surgery and improving the CR rate should still be considered, which positively affects the long-term prognosis of patients in the new treatment mode.

Exploiting the interaction between halloysite and charged PNAs for their controlled release.

The design and development of nanomaterials that could be used in nanomedicine are of fundamental importance to obtain smart nanosystems for the treatment of several diseases. Halloysite, because of its interesting features, represents a suitable nanomaterial for the delivery of different biologically active species. Among them, peptide nucleic acids (PNAs) have attracted considerable attention in recent decades for their potential applications in both molecular antisense diagnosis and as therapeutic agents, although up to now, the actual clinical applications have been very limited. Herein we report a systematic study on the supramolecular interaction of three differently charged PNAs with halloysite. Understanding the interaction mode of charged molecules with the clay surfaces represents a key factor for the future design and development of halloysite based materials which could be used for the delivery and subsequent intracellular release of PNA molecules. Thus, three different PNA tetramers, chosen as models, were synthesized and loaded onto the clay. The obtained nanomaterials were characterized using spectroscopic studies and thermogravimetric analysis, and their morphologies were studied using high angle annular dark field transmission electron microscopy (HAADF/STEM) coupled with Energy Dispersive X-ray spectroscopy (EDX). The aqueous mobility of the three different nanomaterials was investigated by dynamic light scattering (DLS) and ζ-potential measurements. The release of PNA tetramers from the nanomaterials was investigated at two different pH values, mimicking physiological conditions. Finally, to better understand the stability of the synthesized PNAs and their interactions with HNTs, molecular modelling calculations were also performed. The obtained results showed that PNA tetramers interact in different ways with HNT surfaces according to their charge which influences their kinetic release in media mimicking physiological conditions.