Psychosocial distress and health status as risk factors for ten-year major adverse cardiac events and mortality in patients with non-obstructive coronary artery disease

BACKGROUND

We examined the risk of psychosocial distress, including Type D personality, depressive symptoms, anxiety, positive mood, hostility, and health status fatigue and disease specific and generic quality of life for MACE in patients with non-obstructive coronary artery disease (NOCAD).

METHODS

In the Tweesteden mild stenosis (TWIST) study, 546 patients with NOCAD were followed for 10 years to examine the occurrence of cardiac mortality, a major cardiac event, or non-cardiac mortality in the absence of a cardiac event. Cox proportional hazard models were used to examine the impact of psychosocial distress and health status on the occurrence of MACE while adjusting for age, sex, disease severity, and lifestyle covariates.

RESULTS

In total 19% of the patients (mean age baseline = 61, SD 9 years; 52% women) experienced MACE, with a lower risk for women compared to men. Positive mood (HR 0.97, 95%CI 0.95-1.00), fatigue (HR 1.03, 95%CI 1.00-1.06), and physical limitation (HR 0.99, 95%CI 0.98-1.00) were associated with MACE in adjusted models. No significant interactions between sex and psychosocial factors were present. Depressive symptoms were predictive of MACE, but no longer after adjustment.

CONCLUSIONS

In patients with NOCAD fatigue, low positive mood, and a lower physical limitation score were associated with MACE, without marked sex differences. Type D personality, psychosocial factors, and health status were not predictive of adverse outcomes. Reducing psychosocial distress is a valid intervention goal by itself, though it is less likely to affect MACE in patients with NOCAD.

Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review

Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.

Trackable Liposomes for In Vivo Delivery Tracing toward Personalized Medicine Care under NIR Light on Skin Tumor

We report an near-infrared (NIR)-trackable and therapeutic liposome with skin tumor specificity. Liposomes with a hydrodynamic diameter of ∼20 nm are tracked under the vein visualization imaging system in the presence of loaded paclitaxel and NIR-active agents. The ability to track liposome nanocarriers is recorded on the tissue-mimicking phantom model and in vivo mouse veins after intravenous administration. The trackable liposome delivery provides in vitro and in vivo photothermal heat (∼40 °C) for NIR-light-triggered area-specific chemotherapeutic release. This approach can be linked with a real-time vein-imaging system to track and apply area-specific local heat, which hitchhikes liposomes from the vein and finally releases them at the tumor site. We conducted studies on mice skin tumors that indicated the disappearance of tumors visibly and histologically (H&E stains). The ability of nanocarriers to monitor after administration is crucial for improving the effectiveness and specificity of cancer therapy, which could be achieved in the trackable delivery system.

Systematic literature review to inform the EULAR recommendations for the use of imaging in crystal-induced arthropathies in clinical practice

OBJECTIVE

To summarise current data regarding the use of imaging in crystal-induced arthropathies (CiAs) informing a European Alliance of Associations for Rheumatology task force.

METHODS

We performed four systematic searches in Embase, Medline and Central on imaging for diagnosis, monitoring, prediction of disease severity/treatment response, guiding procedures and patient education in gout, calcium pyrophosphate dihydrate deposition (CPPD) and basic calcium phosphate deposition (BCPD). Records were screened, manuscripts reviewed and data of the included studies extracted. The risk of bias was assessed by validated instruments.

RESULTS

For gout, 88 studies were included. Diagnostic studies reported good to excellent sensitivity and specificity of dual-energy CT (DECT) and ultrasound (US), high specificity and lower sensitivity for conventional radiographs (CR) and CT. Longitudinal studies demonstrated sensitivity to change with regard to crystal deposition by US and DECT and inflammation by US and structural progression by CR and CT. For CPPD, 50 studies were included. Diagnostic studies on CR and US showed high specificity and variable sensitivity. There was a single study on monitoring, while nine assessed the prediction in CPPD. For BCPD, 56 studies were included. There were two diagnostic studies, while monitoring by CR and US was assessed in 43 studies, showing a reduction in crystal deposition. A total of 12 studies with inconsistent results assessed the prediction of treatment response. The search on patient education retrieved two studies, suggesting a potential role of DECT.

CONCLUSION

This SLR confirmed a relevant and increasing role of imaging in the field of CiAs.

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

Feasibility Analysis of Individualized Low Flow Rate Abdominal Contrast-Enhanced Computed Tomography in Chemotherapy Patients: Dual-Source Computed Tomography With Low Tube Voltage

PURPOSE

The aim of the study is to investigate the feasibility of using dual-source computed tomography (CT) combined with low flow rate and low tube voltage for postchemotherapy image assessment in cancer patients.

METHODS

Ninety patients undergoing contrast-enhanced CT scans of the upper abdomen were prospectively enrolled and randomly assigned to groups A, B, and C (n = 30 each). In group A, patients underwent scans at 120 kVp with 448 mgI/kg. Patients in group B underwent scans at 100 kVp with 336 mgI/kg. Patient in group C underwent scans at 70 kVp with of 224 mgI/kg. Quantitative measurements including the CT number, standard deviation of CT number, signal-to-noise ratio, contrast-to-noise ratio, subjective reader scores, and the volume and flow rate of contrast agent were evaluated for each group.

RESULTS

There was no statistically significant difference in the subjective image scores within the three groups except for the kidney (all P > 0.05). Group C showed significantly higher CT values, lower noise levels, and higher signal-to-noise ratio and contrast-to-noise ratio values in the majority of the regions of interest compared to the other groups (P < 0.05). In group C, the contrast agent dose was decreased by 46% compared to group A (79.48 ± 12.24 vs 42.7 ± 8.6, P < 0.01), and the contrast agent injection rate was reduced by 22% (2.7 ± 0.41 vs 2.1 ± 0.4, P < 0.01).

CONCLUSIONS

The use of 70 kVp tube voltage combined with low iodine flow rates prove to be a more effective approach in solving the challenge of compromised blood vessels in postchemotherapy tumor patients, without reducing image quality and diagnostic confidence.

Anatomo-physiological basis and applied techniques of electrical neuromodulation in chronic pain

Chronic pain, a complex and debilitating condition, poses a significant challenge to both patients and healthcare providers worldwide. Conventional pharmacological interventions often prove inadequate in delivering satisfactory relief while carrying the risks of addiction and adverse reactions. In recent years, electric neuromodulation emerged as a promising alternative in chronic pain management. This method entails the precise administration of electrical stimulation to specific nerves or regions within the central nervous system to regulate pain signals. Through mechanisms that include the alteration of neural activity and the release of endogenous pain-relieving substances, electric neuromodulation can effectively alleviate pain and improve patients' quality of life. Several modalities of electric neuromodulation, with a different grade of invasiveness, provide tailored strategies to tackle various forms and origins of chronic pain. Through an exploration of the anatomical and physiological pathways of chronic pain, encompassing neurotransmitter involvement, this narrative review offers insights into electrical therapies' mechanisms of action, clinical utility, and future perspectives in chronic pain management.

Increased Cortical Thickness in Alzheimer's Disease

OBJECTIVE

Patients with Alzheimer's disease (AD) have diffuse brain atrophy, but some regions, such as the anterior cingulate cortex (ACC), are spared and may even show increase in size compared to controls. The extent, clinical significance, and mechanisms associated with increased cortical thickness in AD remain unknown. Recent work suggested neural facilitation of regions anticorrelated to atrophied regions in frontotemporal dementia. Here, we aim to determine whether increased thickness occurs in sporadic AD, whether it relates to clinical symptoms, and whether it occur in brain regions functionally connected to-but anticorrelated with-locations of atrophy.

METHODS

Cross-sectional clinical, neuropsychological, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative were analyzed to investigate cortical thickness in AD subjects versus controls. Atrophy network mapping was used to identify brain regions functionally connected to locations of increased thickness and atrophy.

RESULTS

AD patients showed increased thickness in the ACC in a region-of-interest analysis and the visual cortex in an exploratory analysis. Increased thickness in the left ACC was associated with preserved cognitive function, while increased thickness in the left visual cortex was associated with hallucinations. Finally, we found that locations of increased thickness were functionally connected to, but anticorrelated with, locations of brain atrophy (r = -0.81, p < 0.05).

INTERPRETATION

Our results suggest that increased cortical thickness in Alzheimer's disease is relevant to AD symptoms and preferentially occur in brain regions functionally connected to, but anticorrelated with, areas of brain atrophy. Implications for models of compensatory neuroplasticity in response to neurodegeneration are discussed. ANN NEUROL 2024;95:929-940.

Wavelet MRE: Imaging propagating broadband acoustic waves with wavelet-based motion-encoding gradients

PURPOSE

To demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform.

METHODS

A wavelet MRE sequence was developed with motion-encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground-truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90-Hz motion) and steady-state dual-frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel.

RESULTS

In simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study.

CONCLUSION

The uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low-amplitude, transient vibrations as a model for the study of traumatic brain injury.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease

BACKGROUNDS

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context.

OBJECTIVES

Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD.

METHODS

This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter.

FINDINGS

This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD.

CONCLUSION

The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

Pädiatrische Rhinologie

The following review article highlights key topics in pediatric rhinology that are currently the focus in research and at conferences as well as in the interdisciplinary discussion between otorhinolaryngologists and pediatricians. In particular, congenital malformations such as choanal atresia or nasal dermoid cysts are discussed, followed by statements on the current procedures for sinogenic orbital complications as well as on the diagnosis and therapy of chronic rhinosinusitis in children. Furthermore, updates on the role of the ENT specialist in the care for children with cystic fibrosis and primary ciliary dyskinesia are provided.

SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images

Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a 3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for all of the standard performance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation-Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Deep learning supported echocardiogram analysis: A comprehensive review

An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.

A Review of Machine Learning Algorithms for Biomedical Applications

As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.

Performance evaluation of commercial and non-commercial shear wave elastography implementations for vascular applications

BACKGROUND

Shear wave elastography (SWE) is mainly used for stiffness estimation of large, homogeneous tissues, such as the liver and breasts. However, little is known about its accuracy and applicability in thin (∼0.5-2 mm) vessel walls. To identify possible performance differences among vendors, we quantified differences in measured wave velocities obtained by commercial SWE implementations of various vendors over different imaging depths in a vessel-mimicking phantom. For reference, we measured SWE values in the cylindrical inclusions and homogeneous background of a commercial SWE phantom. Additionally, we compared the accuracy between a research implementation and the commercially available clinical SWE on an Aixplorer ultrasound system in phantoms and in vivo in patients.

METHODS

SWE measurements were performed over varying depths (0-35 mm) using three ultrasound machines with four ultrasound probes in the homogeneous 20 kPa background and cylindrical targets of 10, 40, and 60 kPa of a multi-purpose phantom (CIRS-040GSE) and in the anterior and posterior wall of a homogeneous polyvinyl alcohol vessel-mimicking phantom. These phantom data, along with in vivo SWE data of carotid arteries in 23 patients with a (prior) head and neck neoplasm, were also acquired in the research and clinical mode of the Aixplorer ultrasound machine. Machine-specific estimated phantom stiffness values (CIRS phantom) or wave velocities (vessel phantom) over all depths were visualized, and the relative error to the reference values and inter-frame variability (interquartile range/median) were calculated. Correlations between SWE values and target/vessel wall depth were explored in phantoms and in vivo using Spearman's correlations. Differences in wave velocities between the anterior and posterior arterial wall were assessed with Wilcoxon signed-rank tests. Intra-class correlation coefficients were calculated for a sample of ten patients as a measure of intra- and interobserver reproducibility of SWE analyses in research and clinical mode.

RESULTS

There was a high variability in obtained SWE values among ultrasound machines, probes, and, in some cases, with depth. Compared to the homogeneous CIRS-background, this variation was more pronounced for the inclusions and the vessel-mimicking phantom. Furthermore, higher stiffnesses were generally underestimated. In the vessel-mimicking phantom, anterior wave velocities were (incorrectly) higher than posterior wave velocities (3.4-5.6 m/s versus 2.9-5.9 m/s, p ≤ 0.005 for 3/4 probes) and remarkably correlated with measurement depth for most machines (Spearman's ρ = -0.873-0.969, p < 0.001 for 3/4 probes). In the Aixplorer's research mode, this difference was smaller (3.3-3.9 m/s versus 3.2-3.6 m/s, p = 0.005) and values did not correlate with measurement depth (Spearman's ρ = 0.039-0.659, p ≥ 0.002). In vivo, wave velocities were higher in the posterior than the anterior vessel wall in research (left p = 0.001, right p < 0.001) but not in clinical mode (left: p = 0.114, right: p = 0.483). Yet, wave velocities correlated with vessel wall depth in clinical (Spearman's ρ = 0.574-0.698, p < 0.001) but not in research mode (Spearman's ρ = -0.080-0.466, p ≥ 0.003).

CONCLUSIONS

We observed more variation in SWE values among ultrasound machines and probes in tissue with high stiffness and thin-walled geometry than in low stiffness, homogeneous tissue. Together with a depth-correlation in some machines, where carotid arteries have a fixed location, this calls for caution in interpreting SWE results in clinical practice for vascular applications.

Ultrasonic surface acoustic wave elastography: A review of basic theories, technical developments, and medical applications

Physiological and pathological changes in tissues often cause changes in tissue mechanical properties, making tissue elastography an effective modality in medical imaging. Among the existing elastography methods, ultrasound elastography is of great interest due to the inherent advantages of ultrasound imaging technology, such as low cost, portability, safety, and wide availability. However, most current ultrasound elastography methods are based on the bulk shear wave; they can image deep tissues but cannot image superficial tissues. To address this challenge, ultrasonic elastography methods based on surface acoustic waves have been proposed. In this paper, we present a comprehensive review of ultrasound-based surface acoustic wave elastography techniques, including their theoretical foundations, technical implementations, and existing medical applications. The goal is to provide a concise summary of the state-of-the-art of this field, hoping to offer a reliable reference for the further development of these techniques and foster the expansion of their medical applications.

Sonographic appearance of focal liver lesions and likelihood of hepatocellular carcinoma in adult Thais with chronic hepatitis B virus infection

PURPOSE

The objective of our study was to study and compare the sonographic findings of hepatocellular carcinoma (HCC) and benign liver lesions, and apply these to an HCC surveillance program in patient with chronic hepatitis B virus (HBV).

METHODS

Sonographic findings of HCC and benign liver lesions were retrospectively reviewed following diagnosis based on either computer tomography or magnetic resonance imaging from July 2010 to December 2020. Multiple sonographic features were analyzed, including internal echogenicity, rim characteristics, and posterior acoustic enhancement. Associations between sonographic characteristics and HCC were assessed using uni- and multi-variate logistic regression analyses.

RESULTS

Of the focal liver lesions in 337 chronic HBV patients, there were 25 HCC and 410 benign lesions, with median sizes of 1.6 and 1.0 cm, respectively. Three ultrasound patterns, homogeneous hypoechogenicity, heterogeneous echogenicity, and hypoechoic rims were more frequently found in HCC than in benign lesions. Moreover, the hypoechoic rim feature was the only sonographic pattern independently associated with HCC (Odds ratio, 68.05; 95% confidence interval, 7.37-628.10; p-values < 0.001). In a subgroup analysis of the lesions sized 2 cm or smaller, no sonographic findings were associated with HCC.

CONCLUSION

A hypoechoic rim was a sonographic feature independently associated with HCC. These findings may aid in improving HCC detection and guiding management during HCC screening and surveillance with ultrasound.

Worker's exposure to radiation in fluoroscopy, assessing and instruments: A systematic literature review

This review aims to identify and analyze the instruments employed for assessing the overall performance of workers exposed to fluoroscopy during surgical procedures and/or interventional procedures, using the PRISMA methodology. The studies were located through searches conducted on PubMed, Web of Science, Lilacs, ScienceDirect, B-ON, EBSCOhost, and EBSCO Discovery Service on March 27, 2023. Additional studies were identified using backward and forward citation techniques. The PEO strategic model was followed. The search spanned studies published between 2012 and 2022. The quality of the studies underwent assessment using the Joanna Briggs Institute checklist for analytical cross-sectional studies. Out of the 23 studies identified, encompassing 3604 individuals, 12 (52%) addressed the reliability and/or validity of the instruments, while 3 (13%) focused on the development and psychometric testing of the instruments. Only 5 instruments (23%), validated and reliable, exclusively evaluated occupational radiological protection. The predominant dimension covered was knowledge (82%, n = 19). The population was assessed in 18 studies, pre- and post-intervention in 2 studies, and 21 studies provided recommendations or tools for improvement. Individuals in the studies utilized passive dosimeters (ranging from 5% to 98%), thyroid shields (15% to 98%), and aprons or lead skirts/coats (28% to 99%). The evidence quality was moderate (6/8). This study underscores the imperative to enhance compliance with protective and monitoring equipment. Furthermore, additional information is warranted concerning the validity and reliability of the instruments used, as well as the development of instruments that are both valid and reliable.

A Comprehensive Review of Empty Sella and Empty Sella Syndrome

OBJECTIVE

Incidental radiographic findings of an empty sella are prevalent in up to 35% of the general population. While empty sella was initially considered clinically insignificant, a subset of patients exhibits endocrine or neuro-ophthalmologic manifestations which are diagnostic of empty sella syndrome (ESS). Recent studies suggest that more patients are affected by ESS than previously recognized, necessitating a deeper understanding of this condition. This comprehensive review describes a practical approach to evaluating and managing ESS.

METHODS

Literature review was conducted on etiologies and risk factors associated with primary and secondary empty sella, the radiologic features that differentiate empty sella from other sellar lesions, and the role of clinical history and hormone testing in identifying patients with ESS, as well as treatment modalities.

RESULTS

Pituitary function testing for somatotroph, lactotroph, gonadotroph, corticotroph, and thyrotroph abnormalities is necessary when suspecting ESS. While an isolated empty sella finding does not require treatment, ESS may require pharmacologic or surgical interventions to address hormone deficits or intracranial hypertension. Targeted hormone replacement as directed by the endocrinologist should align with guidelines and patient-specific needs. Treatment may involve a multidisciplinary collaboration with neurology, neurosurgery, or ophthalmology to address patient symptoms.

CONCLUSION

This review underscores the evolving understanding of ESS, stressing the significance of accurate diagnosis and tailored management to mitigate potential neurologic and endocrine complications in affected individuals.

New forays into measurement of ocular biomechanics

PURPOSE OF REVIEW

The field of corneal biomechanics has rapidly progressed in recent years, reflecting technological advances and an increased understanding of the clinical significance of measuring these properties. This review will evaluate in-vivo biomechanical properties obtained by current technologies and compare them regarding their relevance to established biomechanical properties obtained by gold-standard ex-vivo techniques normally conducted on elastic materials.

RECENT FINDINGS

Several new technologies have appeared in recent years, including vibrational optical coherence tomography (VOCT) and the corneal indentation device (CID). These techniques provide promising new opportunities for minimally invasive and accurate measurements of corneal viscoelastic properties.

SUMMARY

Alterations in corneal biomechanics are known to occur in several corneal degenerative diseases and after refractive surgical procedures. The measurement of corneal biomechanical properties has the capability to diagnose early disease and monitor corneal disease progression. Several new technologies have emerged in recent years, allowing for more accurate and less invasive measurements of corneal biomechanical properties, most notably the elastic modulus.

Ultrasound Shear Elastography With Expanded Bandwidth (USEWEB): A Novel Method for 2D Shear Phase Velocity Imaging of Soft Tissues

Ultrasound shear wave elastography (SWE) is a noninvasive approach for evaluating mechanical properties of soft tissues. In SWE either group velocity measured in the time-domain or phase velocity measured in the frequency-domain can be reported. Frequency-domain methods have the advantage over time-domain methods in providing a response for a specific frequency, while time-domain methods average the wave velocity over the entire frequency band. Current frequency-domain approaches struggle to reconstruct SWE images over full frequency bandwidth. This is especially important in the case of viscoelastic tissues, where tissue viscoelasticity is often studied by analyzing the shear wave phase velocity dispersion. For characterizing cancerous lesions, it has been shown that considerable biases can occur with group velocity-based measurements. However, using phase velocities at higher frequencies can provide more accurate evaluations. In this paper, we propose a new method called Ultrasound Shear Elastography with Expanded Bandwidth (USEWEB) used for two-dimensional (2D) shear wave phase velocity imaging. We tested the USEWEB method on data from homogeneous tissue-mimicking liver fibrosis phantoms, custom-made viscoelastic phantom measurements, phantoms with cylindrical inclusions experiments, and in vivo renal transplants scanned with a clinical scanner. We compared results from the USEWEB method with a Local Phase Velocity Imaging (LPVI) approach over a wide frequency range, i.e., up to 200-2000 Hz. Tests carried out revealed that the USEWEB approach provides 2D phase velocity images with a coefficient of variation below 5% over a wider frequency band for smaller processing window size in comparison to LPVI, especially in viscoelastic materials. In addition, USEWEB can produce correct phase velocity images for much higher frequencies, up to 1800 Hz, compared to LPVI, which can be used to characterize viscoelastic materials and elastic inclusions.

The Predictive Power of Bosniak 3 and 4 Cystic Renal Lesion Categorization Using Contrast-Enhanced Ultrasound

OBJECTIVES

Contrast-enhanced ultrasound (CEUS) is increasingly utilized for the noninvasive assessment of renal cystic lesions, using the Bosniak grading system. Bosniak 3-4 lesions require surgical referral, which allows correlation with the histopathological outcome.

METHODS

In this single-center, retrospective study we evaluated renal CEUS exams conducted with SonoVue® with a diagnosis of a Bosniak 3 or 4 lesion between 2019 and 2022. A total of 49 patients and 50 lesions met the inclusion criteria, 31 lesions had available histopathological results. Patient demographics, cyst morphology, and dominant imaging features were registered. The histopathological diagnosis was considered a reference standard.

RESULTS

Positive predictive power (PPV) for neoplastic lesions was comparable in the Bosniak 3 and 4 categories (75 vs 93.3%, P = .33), while PPV for histopathologically malignant lesion was considerably higher in the latter group (25 vs 93.33%, P = .0002). None of the lesions which had vividly enhancing thin septa as their dominant CEUS feature were malignant. Oncocytoma, multilocular cystic renal neoplasm of low malignant potential, and cystic nephroma were the major benign entities among Bosniak 3 lesions. Localized cystic kidney disease and hemorrhagic cysts were found to be the primary mimickers leading to false positive imaging findings.

CONCLUSIONS

CEUS has a high predictive power for malignancy in the Bosniak 4 category, which is not maintained in the Bosniak 3 group due to the large proportion of benign lesions. Adherence to rigorous rule-in criteria and active surveillance strategies need to be considered for equivocal CEUS Bosniak 3 lesions.

High-spatial-resolution transcranial focused ultrasound neuromodulation using frequency-modulated pattern interference radiation force

Stimulating the brain in a precise location is crucial in ultrasound neuromodulation. However, improving the resolution proves a challenge owing to the characteristics of transcranial focused ultrasound. In this paper, we present a new neuromodulation system that overcomes the existing limitations based on an acoustic radiation force with a frequency-modulated waveform and standing waves. By using the frequency-modulated pattern interference radiation force (FM-PIRF), the axial spatial resolution can be reduced to a single wavelength level and the target location can be controlled in axial direction electronically. A linear frequency-modulated chirp waveform used in the experiment was designed based on the simulation results. The displacement of the polydimethylsiloxane (PDMS) cantilever was measured at intervals of 0.1 mm to visualize the distribution of radiation force. These results and methods experimentally show that FM-PIRF has improved spatial resolution and capability of electrical movement.

Rare CRHR2 and GRM8 variants identified as candidate factors associated with eating disorders in Japanese patients by whole exome sequencing

Eating disorders (EDs) are a type of psychiatric disorder characterized by pathological eating and related behavior and considered to be highly heritable. The purpose of this study was to explore rare variants expected to display biological functions associated with the etiology of EDs. We performed whole exome sequencing (WES) of affected sib-pairs corresponding to disease subtype through their lifetime and their parents. From those results, rare single nucleotide variants (SNVs) concordant with sib-pairs were extracted and estimated to be most deleterious in the examined families. Two non-synonymous SNVs located on corticotropin-releasing hormone receptor 2 (CRHR2) and glutamate metabotropic receptor 8 (GRM8) were identified as candidate disease susceptibility factors. The SNV of CRHR2 was included within the cholesterol binding motif of the transmembrane helix region, while the SNV of GRM8 was found to contribute to hydrogen bonds for an α-helix structure. CRHR2 plays important roles in the serotoninergic system of dorsal raphe nuclei, which is involved with feeding and stress-coping behavior, whereas GRM8 modulates glutamatergic neurotransmission. Moreover, GRM8 modulates glutamatergic neurotransmission, and is also considered to have effects on dopaminergic and adrenergic neurotransmission. Thus, identification of rare and deleterious variants in this study is expected to increase understanding and treatment of affected individuals. Further investigation regarding the biological function of these variants may provide an opportunity to elucidate the pathogenesis of EDs.

Sacroiliitis in inflammatory bowel disease on abdominal computed tomography: prevalence, misses, and associated factors

OBJECTIVE

To evaluate the prevalence and rate of a missed diagnosis of sacroiliitis on abdominal computed tomography (CT) in patients with inflammatory bowel disease (IBD). Factors associated with sacroiliitis were also assessed.

METHOD

This retrospective study included 210 patients with IBD (mean age 31.1 years) who underwent abdominal CT. Based on a validated abdominal CT scoring tool, bilateral sacroiliac (SI) joints on abdominal CT in the whole study population were retrospectively reviewed. Subsequently, patients were classified into the 'patients with sacroiliitis' group and the 'patients without sacroiliitis' group. Univariate and multivariate regression analyses were used to clarify the factors associated with sacroiliitis.

RESULTS

Sacroiliitis was identified in 26 out of 210 patients (12.4%). However, sacroiliitis was recognized on the primary reading in only five of these 26 patients (19.2%) and was missed on the initial report in the remaining 21 patients (80.8%). Among the 21 patients, 20 (95.2%) were finally diagnosed with axial spondyloarthritis (axSpA). There was a higher prevalence of female sex (p = 0.04), upper gastrointestinal involvement (p = 0.04), and back pain (p < 0.01) in patients with sacroiliitis than in those without sacroiliitis. However, on multivariate analysis, back pain was the only factor associated with sacroiliitis (p = 0.01).

CONCLUSION

Physicians should carefully evaluate SI joints on abdominal CT in patients with IBD to enable early detection of sacroiliitis, potentially leading to an early diagnosis of axSpA. In addition, if patients with IBD present with back pain, the possibility of sacroiliitis should be considered.

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging

RATIONALE AND OBJECTIVES

To develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging.

METHODS

A total of 606 lumbar vertebrae with dual-energy CT imaging and quantitative CT (QCT) evaluation were included in the retrospective study and randomly divided into the training (n = 424) and validation (n = 182) cohorts. Radiomics features and DTL features were extracted from 70-keV monochromatic CT images, followed by feature selection and model construction, radiomics and DTL features models were established. Then, we integrated the selected two types of features into a features fusion model. We developed a two-level classifier for the hierarchical pairwise classification of each vertebra. All the vertebrae were first classified into osteoporosis and non-osteoporosis groups, then non-osteoporosis group was classified into osteopenia and normal groups. QCT was used as reference. The predictive performance and clinical usefulness of three models were evaluated and compared.

RESULTS

The area under the curve (AUC) of the features fusion, radiomics and DTL models for the classification between osteoporosis and non-osteoporosis were 0.981, 0.999, 0.997 in the training cohort and 0.979, 0.943, 0.848 in the validation cohort. Furthermore, the AUCs of the previously mentioned models for the differentiation between osteopenia and normal were 0.994, 0.971, 0.996 in the training cohort and 0.990, 0.968, 0.908 in the validation cohort. The overall accuracy of the previously mentioned models for two-level classifications was 0.979, 0.955, 0.908 in the training cohort and 0.918, 0.885, 0.841 in the validation cohort. Decision curve analysis showed that all models had high clinical value.

CONCLUSION

The feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

Accuracy of linear measurements for implant planning based on low-dose cone beam CT protocols: a systematic review and meta-analysis

OBJECTIVES

The aim of this systematic review was to verify the accuracy of linear measurements performed on low-dose CBCT protocols for implant planning, in comparison with those performed on standard and high-resolution CBCT protocols.

METHODS

The literature search included four databases (Pubmed, Web of Science, Embase, and Scopus). Two reviewers independently screened titles/abstracts and full texts according to eligibility criteria, extracted the data, and examined the methodological quality. Risk of bias assessment was performed using the Quality Assessment Tool For In Vitro Studies. Random-effects meta-analysis was used for pooling measurement error data.

RESULTS

The initial search yielded 4684 titles. In total, 13 studies were included in the systematic review, representing a total of 81 samples, while 9 studies were included in the meta-analysis. The risk of bias ranged from medium to low. The main results across the studies indicate a strong consistency in linear measurements performed on low-dose images in relation to the reference methods. The overall pooled planning measurement error from low-dose CBCT protocols was -0.24 mm (95% CI, -0.52 to 0.04) with a high level of heterogeneity, showing a tendency for underestimation of real values. Various studies found no significant differences in measurements across different protocols (eg, voxel sizes, mA settings, or dose levels), regions (incisor, premolar, molar) and types (height vs. width). Some studies, however, noted exceptions in measurements performed on the posterior mandible.

CONCLUSION

Low-dose CBCT protocols offer adequate precision and accuracy of linear measurements for implant planning. Nevertheless, diagnostic image quality needs must be taken into consideration when choosing a low-dose CBCT protocol.

The Role of 18-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) in the Diagnosis and Evaluation of Spondylodiscitis

Spondylodiscitis, characterized by inflammation of the intervertebral disc and adjacent vertebral bodies, presents a diagnostic challenge due to its nonspecific clinical manifestations and variable imaging findings. This review examines the role of PET-CT with FDG, in the evaluation of spondylodiscitis, focusing on its utility in diagnosis, assessment of disease extent, treatment response monitoring, and prognostication. FDG PET-CT, by combining metabolic and anatomical imaging modalities, offers superior sensitivity and specificity compared to conventional imaging techniques in detecting infectious foci, distinguishing between infection and post-treatment changes, and identifying occult sources of infection. Additionally, FDG PET-CT facilitates the localization of infection, aiding in targeted biopsy and guiding surgical intervention. Moreover, quantitative PET parameters, such as standardized uptake values (SUVs), hold promise for predicting treatment response and prognosis. Despite its advantages, FDG PET-CT has limitations, including false-positive results in the setting of inflammation and limited availability in resource-constrained settings. Collaborative efforts between radiologists, nuclear medicine specialists, infectious disease specialists, and spine surgeons are essential to optimize the role of FDG PET-CT in the multidisciplinary management of spondylodiscitis. Further research is warranted to elucidate the cost-effectiveness and clinical impact of FDG PET-CT in this challenging clinical entity.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases' diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer's diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Multi-spectral photoacoustic imaging combined with acoustic radiation force impulse imaging for applications in tissue engineering

Tissue engineering is a dynamic field focusing on the creation of advanced scaffolds for tissue and organ regeneration. These scaffolds are customized to their specific applications and are often designed to be complex, large structures to mimic tissues and organs. This study addresses the critical challenge of effectively characterizing these thick, optically opaque scaffolds that traditional imaging methods fail to fully image due to their optical limitations. We introduce a novel multi-modal imaging approach combining ultrasound, photoacoustic, and acoustic radiation force impulse imaging. This combination leverages its acoustic-based detection to overcome the limitations posed by optical imaging techniques. Ultrasound imaging is employed to monitor the scaffold structure, photoacoustic imaging is employed to monitor cell proliferation, and acoustic radiation force impulse imaging is employed to evaluate the homogeneity of scaffold stiffness. We applied this integrated imaging system to analyze melanoma cell growth within silk fibroin protein scaffolds with varying pore sizes and therefore stiffness over different cell incubation periods. Among various materials, silk fibroin was chosen for its unique combination of features including biocompatibility, tunable mechanical properties, and structural porosity which supports extensive cell proliferation. The results provide a detailed mesoscale view of the scaffolds' internal structure, including cell penetration depth and biomechanical properties. Our findings demonstrate that the developed multimodal imaging technique offers comprehensive insights into the physical and biological dynamics of tissue-engineered scaffolds. As the field of tissue engineering continues to advance, the importance of non-ionizing and non-invasive imaging systems becomes increasingly evident, and by facilitating a deeper understanding and better characterization of scaffold architectures, such imaging systems are pivotal in driving the success of future tissue-engineering solutions.

No evidence for an association of voxel-based morphometry with short-term non-motor outcomes in deep brain stimulation for Parkinson's disease

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy in advanced Parkinson's disease (PD). Motor and non-motor outcomes, however, show considerable inter-individual variability. Preoperative morphometry-based metrics have recently received increasing attention to explain treatment effects. As evidence for the prediction of non-motor outcomes is limited, we sought to investigate the association between metrics of voxel-based morphometry and short-term non-motor outcomes following STN-DBS in this prospective open-label study. Forty-nine PD patients underwent structural MRI and a comprehensive clinical assessment at preoperative baseline and 6-month follow-up. Voxel-based morphometry was used to assess associations between cerebral volume and non-motor outcomes corrected for multiple comparisons using a permutation-based approach. We replicated existing results associating volume loss of the superior frontal cortex with subpar motor outcomes. Overall non-motor burden, however, was not significantly associated with morphometric features, limiting its use as a marker to inform patient selection and holistic preoperative counselling.

Incidentally found parotid gland lesion in 18F-FDG PET/CT for staging evaluation of patients with hepatocellular carcinoma: remote possibility of metastatic tumor or second primary salivary gland malignancy

OBJECTIVES

We primarily aimed to evaluate whether parotid incidental lesion (PIL) in 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for staging evaluation of patients with hepatocellular carcinoma (HCC) would represent a possibility of extrahepatic metastasis or second primary malignancy (SPM). Additionally, we explored the incidence of PIL in HCC patients and examined any associated risk factors.

METHODS

We retrospectively analyzed patients with HCC who underwent 18F-FDG PET/CT at our institution from 2010 to 2022. The pathological findings of PILs in HCC patients were investigated for confirmatory identification of the risk of HCC metastasis or SPM in parotid gland. Healthy controls received 18F-FDG PET/CT for health screening were also enrolled to compare the incidence of PILs with HCC patients. Various parameters associated with patient demographics and characteristics of HCC were analyzed to find the related factors of PILs.

RESULTS

A total of 17,674 patients with HCC and 2,090 healthy individuals who had undergone 18F-FDG PET/CT scans were enrolled in the analyses. Among the 54 HCC patients who underwent pathological confirmation for PILs, benign primary parotid tumor was most commonly observed (n = 43 [79.6%]); however, no malignant lesions were detected, including HCC metastasis. The incidence of PILs was higher in patients diagnosed with HCC compared with the control group (485 [2.7%] vs. 23 [1.1%], p = 0.002). Analysis for the risk factors for PILs revealed that patient age, sex, and positive viral markers were significantly associated with the incidence of PILs in patients with HCC (all p < 0.001).

CONCLUSIONS

Our study demonstrates that PILs are more frequently identified in patients with HCC on 18F-FDG PET/CT. However, no malignant PIL, including extrahepatic metastasis of HCC, was identified. Therefore, the presence of PIL should not impede or delay the treatment process for patients with HCC. Additionally, we suggested that for future swift and straightforward differential diagnoses of PIL, the development of additional protocols within the PET/CT imaging could be beneficial.

W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations. We theoretically and experimentally determined that combining the concepts of data augmentation optimized for GAN training (DAG) and Wasserstein GAN yields a considerably stable generation of synthetic images and effectively aligns their distribution with that of real images, thereby achieving a high degree of similarity. The classification model was trained using real and synthetic samples. Consequently, the GAN technique used in the diagnostic test had an improved F1 score of approximately 7.8% compared with CA. The final F1 score was 80.24%, and the recall and precision were 84.3% and 88.7%, respectively. The results obtained using the augmented samples outperformed those obtained using pure real samples without augmentation. In addition, we adopted explainable AI techniques that leverage a class activation map (CAM) and principal component analysis to facilitate visual analysis of the network's results. The framework was designed to suggest an attention map and scattering plot to visually explain the disease predictions of the network.

Co-ordinate-based positional embedding that captures resolution to enhance transformer's performance in medical image analysis

Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of locality. In medical imaging, where input data may differ in size and resolution, existing architectures require resampling or resizing during pre-processing, leading to potential spatial resolution loss and information degradation. This study proposes a co-ordinate-based embedding that encodes the geometry of medical images, capturing physical co-ordinate and resolution information without the need for resampling or resizing. The effectiveness of the proposed embedding is demonstrated through experiments with UNETR and SwinUNETR models for infarct segmentation on MRI dataset with AxTrace and AxADC contrasts. The dataset consists of 1142 training, 133 validation and 143 test subjects. Both models with the addition of co-ordinate based positional embedding achieved substantial improvements in mean Dice score by 6.5% and 7.6%. The proposed embedding showcased a statistically significant advantage p-value< 0.0001 over alternative approaches. In conclusion, the proposed co-ordinate-based pixel-wise positional embedding method offers a promising solution for Transformer-based models in medical image analysis. It effectively leverages physical co-ordinate information to enhance performance without compromising spatial resolution and provides a foundation for future advancements in positional embedding techniques for medical applications.

Biodegradable Microspheres for Transarterial Chemoembolization in Malignant Liver Disease

Transarterial chemoembolization (TACE) has revolutionized the treatment landscape for malignant liver disease, offering localized therapy with reduced systemic toxicity. This manuscript delves into the use of degradable microspheres (DMS) in TACE, exploring its potential advantages and clinical applications. DMS-TACE emerges as a promising strategy, offering temporary vessel occlusion and optimized drug delivery. The manuscript reviews the existing literature on DMS-TACE, emphasizing its tolerability, toxicity, and efficacy. Notably, DMS-TACE demonstrates versatility in patient selection, being suitable for both intermediate and advanced stages. The unique properties of DMS provide advantages over traditional embolic agents. The manuscript discusses the DMS-TACE procedure, adverse events, and tumor response rates in HCC, ICC, and metastases.

The student-teacher framework guided by self-training and consistency regularization for semi-supervised medical image segmentation

Due to the high suitability of semi-supervised learning for medical image segmentation, a plethora of valuable research has been conducted and has achieved noteworthy success in this field. However, many approaches tend to confine their focus to a singular semi-supervised framework, thereby overlooking the potential enhancements in segmentation performance offered by integrating several frameworks. In this paper, we propose a novel semi-supervised framework named Pesudo-Label Mean Teacher (PLMT), which synergizes the self-training pipeline with pseudo-labeling and consistency regularization techniques. In particular, we integrate the student-teacher structure with consistency loss into the self-training pipeline to facilitate a mutually beneficial enhancement between the two methods. This structure not only generates remarkably accurate pseudo-labels for the self-training pipeline but also furnishes additional pseudo-label supervision for the student-teacher framework. Moreover, to explore the impact of different semi-supervised losses on the segmentation performance of the PLMT framework, we introduce adaptive loss weights. The PLMT could dynamically adjust the weights of different semi-supervised losses during the training process. Extension experiments on three public datasets demonstrate that our framework achieves the best performance and outperforms the other five semi-supervised methods. The PLMT is an initial exploration of the framework that melds the self-training pipeline with consistency regularization and offers a comparatively innovative perspective in semi-supervised image segmentation.

Maxillary sinus augmentation via the bone lid technique: A prospective, radiographic case series

BACKGROUND

Maxillary sinus augmentation is one of the most performed procedures to increase the bone quantity of the atrophic maxilla to allow implant placement. The aim of the present case series was to describe a surgical protocol to perform maxillary sinus augmentation with the "bone lid technique," and its outcomes in a cohort of patients eligible for the procedure.

METHODS

After the initial clinical evaluation, a cone-beam computed tomography (CBCT) examination was performed for preoperative assessment. Patients were then scheduled for surgical intervention. At 6-9 months follow-up, patients underwent a second CBCT scan to evaluate bone height following bone graft and to schedule implant placement.

RESULTS

A total of 11 patients were enrolled in the study with a total of 13 sinus lift procedures. Membrane perforation was registered in 4 cases (30.76%). Mean surgical time was 67.69 min (SD 6.51). Postoperative period was uneventful in all patients, in the absence of complications. The mean graft volume increase was 2.46 cm3 (SD 0.85), and the mean height increase was 14.27 mm (SD 3.18). Mean membrane thickness was 1.40 mm (SD 0.75). In all the 4 cases with sinus membrane perforation, the membrane had a thickness lower than 1 mm.

CONCLUSIONS

The present study highlights that the maxillary sinus augmentation with bone lid repositioning could provide repeatable results in terms of bone height increase. The technique appears reliable both in terms of bone gain and absence of complications.

KEY POINTS

The bone lid technique for maxillary sinus augmentation provides repeatable results in terms of bone height increase. The favorable clinical outcomes can be related to an enhancement of bone formation due to the unique osteoconductive and osteoinductive properties of autogenous bone, along with a reduction of soft tissue ingrowth. Complications were not observed in any of the patients following the surgical procedures. The risk of Schneiderian membrane perforation is inversely proportional to membrane thickness; the thinner the membrane is, the higher the risk to perforate it.

A robust genetic algorithm-based optimal feature predictor model for brain tumour classification from MRI data

Brain tumour can be cured if it is initially screened and given timely treatment to the patients. This proposed idea suggests a transform- and windowing-based optimization strategy for exposing and segmenting the tumour region in brain pictures. The processes of image processing that are included in the proposed idea include preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation. In order to convert the pixels connected to the spatial domain into a multi-resolution domain, the Gabor transform is first applied to the brain test image. The Gabor converted brain image is then used to extract the parameters of the multi-level features. After that, the Genetic Algorithm (GA) is used to optimize the extracted features, and Neuro Fuzzy System (NFS) is used to classify the optimistic prominent section. Finally, the tumour region in brain images is found and segmented using the normalized segmentation algorithm. The effective detection and classification of brain tumours by the characteristics of sensitivity, specificity, and accuracy are described by the suggested GA-based NFS classification approach. The trial findings are displayed with an average of 99.37% sensitivity, 98.9% specificity, 99.21% accuracy, 97.8% PPV, 91.8% NPV, 96.8% FPR, and 90.4% FNR.

Myxofibrosarcoma of the Chest Wall Detected on 99mTc-MDP Whole-Body Bone Scan

Myxofibrosarcoma is a type of soft tissue sarcoma, predominantly characterized by a high propensity for local recurrence, albeit demonstrating a relatively diminished risk for distant metastasis. Its prevalence is notably higher in elderly patients. Here, we present a case of a 73-year-old woman diagnosed with Myxofibrosarcoma. She was subjected to a whole-body bone scan using 99mTc-methylene diphosphonate (MDP) to survey potential bony metastasis. It revealed marked MDP accumulation with peripheral soft tissue uptake in the right lateral chest region of this patient. This imaging phenotype could potentially be attributed to the augmented vascularity within the tumor, a finding that was prominently displayed in this particular case.

Assessing the causal associations of different types of statins use and knee/hip osteoarthritis: A Mendelian randomization study

OBJECTIVE

This study comprehensively evaluated the causal relationship between different types of statins use and knee/hip osteoarthritis (OA) using a two-sample and multivariate Mendelian randomization (MR) method.

METHODS

MR analysis was conducted using publicly available summary statistics data from genome-wide association studies (GWAS) to assess the causal associations between total statins use (including specific types) and knee/hip OA. The primary analysis utilized the inverse variance-weighted (IVW) method, with sensitivity analysis conducted to assess robustness. Multivariable MR (MVMR) analysis adjusted for low-density lipoprotein cholesterol (LDL-C), intermediate-density lipoprotein cholesterol (IDL-C), high-density lipoprotein cholesterol (HDL-C), and body mass index (BMI).

RESULTS

The MR analysis revealed a significant inverse association between genetically predicted total statins use and the risk of knee OA (OR = 0.950, 95%CI: 0.920-0.982, p = 0.002) as well as hip OA (OR = 0.932, 95%CI: 0.899-0.966, p <0.001). Furthermore, this study highlighted a reduced risk of knee/hip OA with the use of atorvastatin and simvastatin. Rosuvastatin use was associated with a decreased risk of hip OA but showed no association with knee OA. MVMR results indicated no correlation between exposure factors and outcomes after adjusting for LDL-C or IDL-C. HDL-C may not significantly contribute to statin-induced osteoarthritis, while BMI may play an important role.

CONCLUSION

This study provides compelling evidence of the close relationship between statin use and a reduced risk of knee/hip OA, particularly with atorvastatin and simvastatin. LDL-C and IDL-C may mediate these effects. These findings have important implications for the clinical prevention and treatment of knee/hip OA.

Ultrasonic synthesis of green lipid nanocarriers loaded with Scutellaria barbata extract: a sustainable approach for enhanced anticancer and antibacterial therapy

Ultrasonic manufacturing has emerged as a promising eco-friendly approach to synthesize lipid-based nanocarriers for targeted drug delivery. This study presents the novel ultrasonic preparation of lipid nanocarriers loaded with Scutellaria barbata extract, repurposed for anticancer and antibacterial use. High-frequency ultrasonic waves enabled the precise self-assembly of DSPE-PEG, Span 40, and cholesterol to form nanocarriers encapsulating the therapeutic extract without the use of toxic solvents, exemplifying green nanotechnology. Leveraging the inherent anticancer and antibacterial properties of Scutellaria barbata, the study demonstrates that lipid encapsulation enhances the bioavailability and controlled release of the extract, which is vital for its therapeutic efficacy. Dynamic light scattering and transmission electron microscopy analyses confirmed the increase in size and successful encapsulation post-loading, along with an augmented negative zeta potential indicating enhanced stability. A high encapsulation efficiency of 91.93% was achieved, and in vitro assays revealed the loaded nanocarriers' optimized release kinetics and improved antimicrobial potency against Pseudomonas aeruginosa, compared to the free extract. The combination of ultrasonic synthesis and Scutellaria barbata in an eco-friendly manufacturing process not only advances green nanotechnology but also contributes to sustainable practices in pharmaceutical manufacturing. The data suggest that this innovative nanocarrier system could provide a robust platform for the development of nanotechnology-based therapeutics, enhancing drug delivery efficacy while aligning with environmental sustainability.

Desmoplastic fibroma in a child: a 9-year follow-up case report

BACKGROUND

Desmoplastic fibroma is an extremely rare primary bone tumor. Its characteristic features include bone destruction accompanied by the formation of soft tissue masses. This condition predominantly affects individuals under the age of 30. Since its histology is similar to desmoid-type fibromatosis, an accurate diagnosis before operation is difficult. Desmoplastic fibroma is resistant to chemotherapy, and the efficacy of radiotherapy is uncertain. Surgical excision is preferred for treatment, but it entails high recurrence. Further, skeletal reconstruction post-surgery is challenging, especially in pediatric cases.

CASE PRESENTATION

Nine years ago, a 14-year-old male patient presented with a 4-year history of progressive pain in his left wrist. Initially diagnosed as fibrous dysplasia by needle biopsy, the patient underwent tumor resection followed by free vascularized fibular proximal epiphyseal transfer for wrist reconstruction. However, a histological examination confirmed a diagnosis of desmoplastic fibroma. The patient achieved bone union and experienced a recurrence in the ipsilateral ulna 5 years later, accompanied by a wrist deformity. He underwent a second tumor resection and wrist arthrodesis in a single stage. The most recent annual follow-up was in September 2023; the patient had no recurrence and was satisfied with the surgery.

CONCLUSIONS

Desmoplastic fibroma is difficult to diagnose and treat, and reconstruction surgery after tumor resection is challenging. Close follow-up by experienced surgeons may be beneficial for prognosis.

Development of a multi-fusion convolutional neural network (MF-CNN) for enhanced gastrointestinal disease diagnosis in endoscopy image analysis

Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients

Background

Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells.

Methods

We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves.

Results

We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set.

Conclusion

Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

AVD-YOLOv5: a new lightweight network architecture for high-speed aortic valve detection from a new and large echocardiography dataset

Heart disease detection is currently gaining widespread attention as a means to enhance the accuracy of cardiologists' diagnoses from cardiac images and reduce diagnosis time. Although high-resolution computed tomography (CT) images are typically favored for heart disease detection, the drawbacks of cost and radiation exposure to patients necessitate alternative approaches. In this context, utilizing ultrasound images becomes pivotal to mitigate radiation risks and maintain cost-effectiveness. In this paper, we propose a novel lightweight model, AVD-YOLOv5, designed for automated aortic valve detection on echocardiography images. This model incorporates several enhancements to the YOLOv5 architecture. Notably, the depth-wise separable convolution significantly contributes to the model's lightweight design by reducing the number of parameters while maintaining precision. We have also created a new and larger dataset comprising 260 echocardiography images specifically for aortic valve detection. Experimental results indicate that the precision value of the modified ADV-YOLOv5 model stands at 94.3%, with a recall value of 86.8%. The model also demonstrates a notable 67% reduction in inference time compared to the original YOLOv5 model. Although there is a marginal reduction in precision by 0.94%, the model's efficiency is significantly increased. The proposed system can be used by cardiologists for more efficient and reliable diagnosis.

Integrating Clinical Neuropsychology and Psychotic Spectrum Disorders: A Systematic Analysis of Cognitive Dynamics, Interventions, and Underlying Mechanisms

Background and Objectives: The study aims to provide a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It focuses on the critical aspects of cognitive impairments, diagnostic tools, intervention efficacy, and the roles of genetic and environmental factors in these disorders. The paper emphasizes the diagnostic significance of neuropsychological tests in identifying cognitive deficiencies and their predictive value in the early management of psychosis. Materials and Methods: The study involved a systematic literature review following the PRISMA guidelines. The search was conducted in significant databases like Scopus, PsycINFO, PubMed, and Web of Science using keywords relevant to clinical neuropsychology and psychotic spectrum disorders. The inclusion criteria required articles to be in English, published between 2018 and 2023, and pertinent to clinical neuropsychology's application in these disorders. A total of 153 articles were identified, with 44 ultimately included for detailed analysis based on relevance and publication status after screening. Results: The review highlights several key findings, including the diagnostic and prognostic significance of mismatch negativity, neuroprogressive trajectories, cortical thinning in familial high-risk individuals, and distinct illness trajectories within psychosis subgroups. The studies evaluated underline the role of neuropsychological tests in diagnosing psychiatric disorders and emphasize early detection and the effectiveness of intervention strategies based on cognitive and neurobiological markers. Conclusions: The systematic review underscores the importance of investigating the neuropsychological components of psychotic spectrum disorders. It identifies significant cognitive impairments in attention, memory, and executive function, correlating with structural and functional brain abnormalities. The paper stresses the need for precise diagnoses and personalized treatment modalities, highlighting the complex interplay between genetic, environmental, and psychosocial factors. It calls for a deeper understanding of these neuropsychological processes to enhance diagnostic accuracy and therapeutic outcomes.

The usefulness of artificial intelligence in breast reconstruction: a systematic review

BACKGROUND

Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction.

METHODS

A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction.

RESULTS

A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification.

CONCLUSIONS

In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

BACKGROUND

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.

METHODS

We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.

RESULTS

The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.

CONCLUSIONS

The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Explainable AI-driven model for gastrointestinal cancer classification

Although the detection procedure has been shown to be highly effective, there are several obstacles to overcome in the usage of AI-assisted cancer cell detection in clinical settings. These issues stem mostly from the failure to identify the underlying processes. Because AI-assisted diagnosis does not offer a clear decision-making process, doctors are dubious about it. In this instance, the advent of Explainable Artificial Intelligence (XAI), which offers explanations for prediction models, solves the AI black box issue. The SHapley Additive exPlanations (SHAP) approach, which results in the interpretation of model predictions, is the main emphasis of this work. The intermediate layer in this study was a hybrid model made up of three Convolutional Neural Networks (CNNs) (InceptionV3, InceptionResNetV2, and VGG16) that combined their predictions. The KvasirV2 dataset, which comprises pathological symptoms associated to cancer, was used to train the model. Our combined model yielded an accuracy of 93.17% and an F1 score of 97%. After training the combined model, we use SHAP to analyze images from these three groups to provide an explanation of the decision that affects the model prediction.

Abnormal expression of Krüppel-like transcription factors and their potential values in lung cancer

Lung cancer still is one of the most common malignancy tumors in the world. However, the mechanisms of its occurrence and development have not been fully elucidated. Zinc finger protein family (ZNFs) is the largest transcription factor family in human genome. Recently, the more and more basic and clinical evidences have confirmed that ZNFs/Krüppel-like factors (KLFs) refer to a group of conserved zinc finger-containing transcription factors that are involved in lung cancer progression, with the functions of promotion, inhibition, dual roles and unknown classifications. Based on the recent literature, some of the oncogenic KLFs are promising molecular biomarkers for diagnosis, prognosis or therapeutic targets of lung cancer. Interestingly, a novel computational approach has been proposed by using machine learning on features calculated from primary sequences, the XGBoost-based model with accuracy of 96.4 % is efficient in identifying KLF proteins. This paper reviews the recent some progresses of the oncogenic KLFs with their potential values for diagnosis, prognosis and molecular target in lung cancer.