Dimensional Images Research Articles

Preparing for downstream tasks in AI for dental radiology: a baseline performance comparison of deep learning models.

To compare the performance of the convolutional neural network (CNN) with the vision transformer (ViT) and the gated multilayer perceptron (gMLP) in the classification of radiographic images of dental structures. Retrospectively collected 2-dimensional images derived from cone beam computed tomographic volumes were used to train CNN, ViT and gMLP architectures as classifiers for 4 different cases. Cases selected for training the architectures were the classification of the radiographic appearance of maxillary sinuses, maxillary and mandibular incisors, presence or absence of the mental foramen and the positional relationship of the mandibular third molar to the inferior alveolar nerve canal. The performance metrics (sensitivity, specificity, precision, accuracy and f1-score) and area under curve (AUC) - receiver operating characteristic and precision-recall curves were calculated. The ViT with an accuracy of 0.74-0.98, performed on par with the CNN model (accuracy 0.71-0.99) in all tasks. The gMLP displayed marginally lower performance (accuracy 0.65-0.98) as compared to the CNN and ViT. For certain tasks, the ViT outperformed the CNN. The AUCs ranged from 0.77-1.00 (CNN), 0.80-1.00 (ViT) and 0.73-1.00 (gMLP) for all of the 4 cases. The difference in performance of the ViT, gMLP and the CNN (the current state-of-the-art) was significant in certain tasks. This difference in model performance for various tasks proves that capabilities of different architectures may be leveraged. The vision transformer, followed by the gated multilayer perceptron are deep learning models that exhibit comparable performance with the convolutional neural network in the classification of dental radiographic images.

Classification of Liver Lesion Stages using pyRadiomics Features Combined with 3D-CNN in 3D-CT and US Images

Objectives: The goal of this study is to identify the various stages in liver diseases employing pyRadiomics features by analyzing the 3-dimensional CT and US images. Methods: The study uses Gray level Run Length Matrix for feature extraction and 3D-CNN for classification and compared with methods like Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) classifiers to assess the efficiency of the proposed model. The suggested algorithm is implemented using the library functions in Tensorflow and Keras packages in Python. Findings: The study utilizes radiomic feature extraction and two-phase classification to detect and classify liver disorders. In the first phase, binary classification is used to classify US and CT images as normal or abnormal, with accuracy rates of 95.4% and 97.9%. This is followed by the second phase classification, which classifies the lesion stages for both US and CT aberrant images. CT images have been shown to be more accurate in categorizing lesion stages than US imaging. CT images have a much lower misclassification rate at each step than US images. Finally, the effectiveness of the 3D-CNN model is compared with SVM, RF, and NB and the suggested method outperforms the other methods. Novelty: The suggested architecture employs radionics feature extraction with two-phase CNN for classification and the suggested method is found to be more efficient than SVM, RF, and NB. Keywords: Radiomics; Feature Extraction; Convolutional Neural Networks (CNN); Classification; Liver Diseases

VEGFC Overexpression in Kidney Progenitor Cells Is a Model of Renal Lymphangiectasia.

Lymphangiogenesis is believed to be a protective response in the setting of multiple forms of kidney injury and mitigates the progression of interstitial fibrosis. To augment this protective response, promoting kidney lymphangiogenesis is being investigated as a potential treatment to slow the progression of kidney disease. As injury-related lymphangiogenesis is driven by signaling from the receptor VEGFR3 (vascular endothelial growth factor receptor 3) in response to the cognate growth factor VEGF (vascular endothelial growth factor)-C released by tubular epithelial cells, this signaling pathway is a candidate for future kidney therapeutics. However, the consequences to kidney development and function to targeting this signaling pathway remain poorly defined. We generated a new mouse model expressing Vegfc under regulation of the nephron progenitor Six2Cre driver strain (Six2Vegfc). Mice underwent a detailed phenotypic evaluation. Whole kidneys were processed for histology and 3-dimensional imaging. Six2Vegfc mice had reduced body weight and kidney function compared with littermate controls. Six2Vegfc kidneys demonstrated large peripelvic fluid-filled lesions with distortion of the pelvicalcyceal system which progressed in severity with age. Three-dimensional imaging showed a 3-fold increase in total cortical vascular density. Histology confirmed a substantial increase in LYVE1+ (lymphatic vessel endothelial hyaluronan receptor-1)/PDPN+ (podoplanin)/VEGFR3+ lymphatic capillaries extending alongside EMCN+ (endomucin) peritubular capillaries. There was no change in EMCN+ peritubular capillary density. Kidney lymphatic density was robustly increased in the Six2Vegfc mice. There were no changes in peritubular blood capillary density despite these endothelial cells also expressing VEGFR3. The model resulted in malformation of the lymphatic hilar plexus, resulting in severe hydronephrosis that resembled a human condition termed renal lymphangiectasia. This study defines the vascular consequences of augmenting VEGFC signaling during kidney development and provides new insight into human renal lymphatic malformations.

A study on analytical solutions of one of the important shallow water wave equations and its stability analysis

Abstract The present research uses the modified w g -expansion technique to give analytical solutions for the nonlinear time fractional integro-differential Kadomtsev-Petviashvili (KP) hierarchy equation with beta fractional derivative. The modified w g -expansion technique is an important method for finding analytical solutions to nonlinear equations. The suggested approach produces three sorts of solutions: trigonometric, hyperbolic, and rational. These answers have been discovered with the aid of a software tool. Additionally, stability analysis of observed governing model solutions is used to validate scientific calculations. Furthermore, the 3-dimensional, contour, and 2-dimensional images are provided for a better understanding of the behavior of the solutions.

Current and Future Directions of Technology in Assessment of Peyronie's Disease.

This review assesses the effectiveness of various imaging modalities for diagnosing and managing Peyronie's disease (PD), exploring their clinical utility and limitations. Ultrasonography, currently the only modality endorsed by the American Urological Association, effectively detects calcifications and assesses vascular status but struggles with identifying non-palpable plaques. Computed tomography and magnetic resonance imaging provide detailed anatomical views but are costly along with other deterring factors. Autophotography and 3-dimensional imaging offer convenient home assessments but with variable accuracy. Elastography improves upon traditional ultrasonography, while emerging AI technologies show promise for automating precise curvature assessments, pending further validation. A combined approach tailored to each individual patient based on their needs enhances diagnostic accuracy. Supplementing ultrasonography with elastography and potentially AI could improve diagnostic outcomes and better guide treatment decisions. Continued research is vital to integrate these advancements into standard clinical practice and develop updated standardized protocols.

Machine learning‐driven microwave imaging for soil moisture estimation near leaky pipe

AbstractCharacterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%.

Normal size of retropharyngeal lymph nodes in children on three dimensional magnetic resonance imaging.

Age-specific normal measurements or specific size criteria for retropharyngeal lymph nodes in children have not been defined. We aimed to determine the normal measurements and distribution of retropharyngeal lymph nodes on three-dimensional magnetic resonance imaging (3-D MRI) in children. In this retrospective study, we included 440 patients (213 girls) aged 0-17years who were admitted to our center with seizures and headaches and underwent brain and neck MRI with T2-sampling perfection with application-optimized contrasts using different flip angle evolution sequences. We evaluated the number, laterality, and level distribution of lateral and medial group lymph nodes according to the skull base-cervical vertebrae. For both groups, we measured the short and long diameters of the largest lymph node in the axial plane and the craniocaudal diameter in the sagittal plane. The short/long diameter ratios and volumes were determined. In 433 cases (98%), 1,554 lateral group lymph nodes were detected. Medial group lymph nodes were less common (7%). The lateral group was mostly bilateral, while the medial group was unilateral. Lateral group lymph nodes extended from the skull base-first cervical (C1) vertebral level to the C4, while the medial group extended from the C2 vertebral level to the C4. The mean axial short axis, axial long axis, and sagittal long axis diameters were 5.8mm, 10.1mm, and 15.5mm for the lateral group and 3.8mm, 7.6mm, and 10.8mm for the medial group, respectively. The findings of the study show that normally retropharyngeal lymph nodes are frequently seen in children and provide valuable information for lateral and medial lymph nodes depending on age.

4D imaging of the volcano feeding system beneath the urban area of the Campi Flegrei caldera

This paper describes an approach to analyze ground deformation data collected by InSAR (Interferometric Synthetic Aperture Radar) imaging the volcano feeding system (VFS) beneath a caldera. The approach is applied to the Campi Flegrei caldera in southern Italy, a densely populated area at high risk for volcanic eruption. The method is a 4D tomographic inversion that considers a combination of 3D pressure sources and dislocations (strike-slip, dip-slip and tensile) acting simultaneously. This is in contrast to traditional methods that assume a priori geometries and type for the volcanic source. Another novelty is that we carry out a time-series analysis of multifrequency InSAR displacement data. The analysis of these multiplatform and multifrequency InSAR data from 2011 to 2022 reveals an inflating source at a depth of 3–4 km that is interpreted as a pressurized magmatic intrusion. The source broadens and migrates laterally over time, with a possible new magmatic pulse arriving in 2018–2020. The model also identifies a shallow region (at 400 m depth) that may be feeding fumaroles in the area. The analysis also reveals a zone of weakness (dip-slip) that could influence the path of rising magma. This method provides a more detailed dynamic 4 - dimensional image of the VFS than previously possible and could be used to improve hazard assessments in active volcanic areas.

2D nonlinear inversion of DC resistivity measurements, a case study; southeastern part of Ras El Dabaa, Northwestern coast, Egypt

The southern Mediterranean coast suffers from limited water resources as a result of exploitation of water supply, population growth, and climate change. Spatial lineaments and Seawater Intrusion (SWI) were detected at the southeast portion of Ras El Dabaa, on Egypt’s northwest coast, using the direct current resistivity (DCR) method. The Vertical Electrical Sounding (VES) data were acquired using Schlumberger array along four profiles and inverted both independently and jointly, aiming to obtain Two Dimensional (2D) geoelectrical images. The results of the one Dimensional (1D) inversion of VES data at each profile were stitched to form pseudo-2D sections on which the resistivity values and aquifer thickness in the southwest of the region appeared to be generally increasing, indicating a potential improvement in water quality.However, the results did not fully image the lateral variation but focused on the horizontal boundaries of the subsurface. On the contrary, the results of 2D inversion of the same data sets successfully managed to provide images that depicted resistivity distribution in both lateral and vertical directions. The detected sets of lineaments and fractured zones within the oolitic limestone and fossiliferous limestone units control the occurrence of groundwater in the region. The 2D inversion scheme revealed a low resistivity zone that indicated the presence of SWI and/or the dissolution of marine salts from the marine limestone bedrock of these aquifers in the northern portions of the studied area. Additionally, analysis of the 2D apparent porosity section shows how aquifers are connected by secondary porosity, which is defined by structures that resemble channels. The current approach offers valuable structural information for future planning and development of such complex geological coastal locations, taking into consideration the vulnerability of the groundwater system.

Prediction of Malignancy and Pathological Types of Solid Lung Nodules on CT Scans Using a Volumetric SWIN Transformer.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules. It is critical to accurately identify solid lung nodules with lobulation and spiculation signs before surgery; however, traditional diagnostic imaging is prone to misdiagnosis, and studies on artificial intelligence-assisted diagnosis are few. Therefore, we introduce a volumetric SWIN Transformer-based method. It is a multi-scale, multi-task, and highly interpretable model for distinguishing between benign solid lung nodules with lobulation and spiculation signs, lung adenocarcinomas, and lung squamous cell carcinoma. The technique's effectiveness was improved by using 3-dimensional (3D) computed tomography (CT) images instead of conventional 2-dimensional (2D) images to combine as much information as possible. The model was trained using 352 of the 441 CT image sequences and validated using the rest. The experimental results showed that our model could accurately differentiate between benign lung nodules with lobulation and spiculation signs, lung adenocarcinoma, and squamous cell carcinoma. On the test set, our model achieves an accuracy of 0.9888, precision of 0.9892, recall of 0.9888, and an F1-score of 0.9888, along with a class activation mapping (CAM) visualization of the 3D model. Consequently, our method could be used as a preoperative tool to assist in diagnosing solitary solid lung nodules with lobulation and spiculation signs accurately and provide a theoretical basis for developing appropriate clinical diagnosis and treatment plans for the patients.

Choroid plexus enlargement in patients with end-stage renal disease: implications for glymphatic system dysfunction.

The choroid plexus plays a role in eliminating detrimental metabolites from the brain as an integral component of the glymphatic system. This study aimed to investigate alterations in choroid plexus volume in patients with end-stage renal disease (ESRD) compared with healthy controls. We enrolled 40 patients with ESRD and 42 healthy controls. They underwent brain magnetic resonance imaging (MRI), specifically using three dimensional T1-weighted imaging. We analyzed choroid plexus volumes and compared them between patients with ESRD and healthy controls. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated. We compared the DTI-ALPS index between the ESRD patients and healthy controls. Additionally, we evaluated the association between choroid plexus volume and neuropsychological tests results in patients with ESRD. There were significant differences in choroid plexus volumes between patients with ESRD and healthy controls. The choroid plexus volumes in patients with ESRD were higher than those in healthy controls (1.392 vs. 1.138%, p < 0.001). The DTI-ALPS index in patients with ESRD was lower than that in healthy controls (1.470 ± 0.239 vs. 1.641 ± 0.266, p = 0.005). There were no differences in choroid plexus volumes between patients with ESRD, regardless of the presence of cognitive impairment. However, among the neuropsychological tests, the scores for word-list recognition in verbal memory were negatively correlated with the choroid plexus volume (r = -0.428, p = 0.006). We demonstrated a significant enlargement of the choroid plexus volume in patients with ESRD compared to healthy controls. This finding suggests that patients with ESRD have glymphatic system dysfunction, which may be related to cognitive impairment.

Three-dimensional Stereoscopic Evaluation and Quantitative Gray Scale Assessment of the Nasolabial Fold and Its Application in Injection Therapy

With the growing demand for aesthetic enhancements, particularly filler injections for nasolabial folds (NLFs), there is a need for more objective and quantitative assessment methods beyond the traditional subjective scales like the Wrinkle Severity Rating Scale (WSRS). This study introduces a novel evaluation method combining 3-dimensional (3D) imaging and grayscale analysis to objectively assess NLF severity and treatment outcomes. A total of 100 healthy participants (86 females, 14 males; mean age 41.85±11.53 years) were assessed using WSRS scores and quantitative 3D measurements. The results demonstrated a significant positive correlation between WSRS scores and gray values (r=0.985, P &lt; 0.05). Significant differences in gray values and 3D linear measurements were observed across WSRS score groups, with high intra-rater reliability confirmed by intraclass correlation coefficients. In addition, 54 patients (mean age 42.9±8.9 y) with moderate to severe NLFs received hyaluronic acid injections. One month post-treatment, WSRS scores significantly decreased (from 3.74±0.45 to 1.63±0.59, P &lt; 0.05), and significant improvements were observed in several 3D measurements. The average gray value also decreased significantly (P &lt; 0.05), indicating a reduction in wrinkle depth. These findings were supported by representative photos showing patient satisfaction and no severe complications. This study validates the use of 3D imaging and grayscale analysis as reliable tools for objectively assessing NLF severity and monitoring treatment efficacy, potentially improving the precision and reliability of aesthetic evaluations in clinical practice.

Computer Design of the Regulating Working Element of the Channel Cleaning Equipment

Cleaning devices of channel systems used for irrigation of agricultural plants and mechatronic parts regulating their operations were analyzed, the issues of automation of construction design, measurement and management in this field were examined and the purpose of the work was determined. As the goal of the work, the issue of automation of the construction design, technological measurement and management process of the working body for efficient cleaning of small and medium-sized concrete-covered channels, bathtubs from sludge and weeds was set. A complex scheme of information, algorithmic and software support for the automation of the design, technological measurement and control of the mechatronic parts of the working body used for cleaning the channel and bathtub was proposed, and the issues of logical modeling and simulation of the drawing of 2- and 3-dimensional images of the working body were solved. Algorithms were developed using the production modeling method to automate the process of technological measurement, implementation and management, which accurately regulates the operations of the working body of the mechanical device of complex construction. In Solid Edge 2D, 3D software environments, the determination of the geometric shapes of the individual mechanical parts of the channel cleaning unit with a milling cutter, their sizes and types of materials is ensured, and the algorithm was developed using the logical modeling method. For the automation of the structural design process, the information security of the mechanical parts of the application object and the elements of the new design milling cutter device was created for the management of the database of drawings.

Fast error detection method for additive manufacturing process monitoring using structured light three dimensional imaging technique

This paper presents a novel method to speed up error detection in an additive manufacturing (AM) process by minimizing the necessary three-dimensional (3D) reconstruction and comparison. We develop a structured light 3D imaging technique that has native pixel-by-pixel mapping between the captured two-dimensional (2D) image and the reconstructed 3D point cloud. This 3D imaging technique allows error detection to be performed in the 2D image domain prior to 3D point cloud generation, which drastically reduces complexity and computational time. Compared to an existing AM error detection method based on 3D reconstruction and point cloud processing, experimental results from a material extrusion (MEX) AM process demonstrate that our proposed method significantly increases the error detection speed.

Pelvic congestion syndrome analysis through quantitative 2-dimensional phase-contrast magnetic resonance imaging: A promising vision from an observational cohort study.

To examine the application of quantitative 2-dimensional phase-contrast magnetic resonance imaging (2D PC-MRI) for treating patients with pelvic congestion syndrome (PCS). We conducted a retrospective cross-sectional analysis by using quantitative 2D PC-MRI data enrolled between April 2017 and Sep 2023. In addition, 32 healthy female controls (HCs) were included. Most patients with PCS presented with chronic pelvic pain and more than half had extra-pelvic venous symptoms (80/81, 98% and 45/81, 56%, respectively). Quantitative 2D PC-MRI analyzed the 81 patients with PCS, 239 patients without PCS, and 32 HCs. The patients with PCS had higher stroke volume (SV), absolute SV (ASV), and mean flux (MF) in the calf region (interstitial pixel shift) than did the HCs. In the left gonadal vein, the patients with PCS had higher SV, backward flow volume (BFV), ASV, and MF and lower forward flow volume (FFV), stroke distance (SD), and mean velocity (MV) than did the HCs. However, the patients with PCS had lower SV, FFV, MF, SD, and MV in the great saphenous veins. Quantitative 2D PC-MRI analysis revealed that the PCS group had higher SV, FFV, BFV, ASV, and MF in the calf region than did the non-PCS group. The variables that most strongly differentiated the patients with PCS from the HCs were SV in the great saphenous veins, SD in the great saphenous veins and left gonadal vein, and MV in the great saphenous veins and left gonadal vein. Caudal flow in the left gonadal vein was identified in half of the patients with PCS (39/81, 48.1%); 14 of them received embolization for left gonadal vein. In additional to providing an objective 3-dimensional morphology of the pelvic veins and extra-pelvic leaks, quantitative 2D PC-MRI analysis reveals distinct hemodynamic profiles between patients with PCS, those without PCS, and HCs, especially in the gonadal veins and regional perfusion of the calves.

Comprehensive Dynamic 3-Dimensional Analysis of the Tricuspid Valve in Functional Tricuspid Regurgitation: Implications for Prophylactic Tricuspid Valve Intervention

ObjectivesUnderstanding of changes in tricuspid annulus (TA) during secondary or functional tricuspid regurgitation (TR) is currently limited and its surgical management relies on limited 2-dimensional (2D) imaging. The aims of the study were to: 1) track and measure changes in TA using 3-dimensional (3D) echocardiography during complete cardiac cycle in patients with functional TR when compared to no TR, and 2) compare TAPSE derived from 2D and 3D coordinates as a measure of RV function when compared to standard method of fractional area change (FAC). DesignIntraoperative 3D echocardiography data was collected prospectively followed by post processing software analysis to track and reconstruct changes throughout the cardiac cycle. SettingData was collected from 108 patients undergoing left sided heart surgery at two large academic centers – Beth Israel Deaconess Medical Center in Boston, MA and Rhode Island Hospital, Providence, RI between from November 2018 to April 2020. ParticipantsFinal dataset (n=92) included two groups: no significant functional tricuspid regurgitation (NTR) group (n=74), defined as ≤ mild tricuspid regurgitation (TR) and TA < 35 mm, and a significant functional TR (FTR) group (n=18), defined as ≥ moderate TR. Interventions3D TEE datasets were analyzed, and the motion of TA coordinates was tracked during complete cardiac cycle in 2D and 3D planes using post-processing and software analysis. Computational modeling of TA motion was performed using computer-aided design. In further analysis, reconstructed and 3D printed models of TV were developed for the two groups. Measurements and Main ResultsPatients in FTR group had larger TA size during the cardiac cycle with less overall excursion and reduced annular dynamism. 3D motion of TA for lateral (L), anterolateral (AL) and posterolateral (PL) coordinates was lower in FTR group compared to NTR group [(18±6.8 vs 13.6±8.5, p=0.02), (15.2±5.5 vs 11.3±6.0, p=0.009) and (17.6±6.6 vs 12.3±5.2, p=0.002) respectively]. TAPSE derived from 3D planes was more accurate for RV function assessment (AUC 0.704, p = 0.011) than 2D TAPSE (AUC 0.625, p = 0.129). Finally, in the FTR group, the anteroseptal-posterolateral (AS-PL) diameter was consistently larger during all phases of cardiac cycle, in comparison with septo-lateral (S-L) diameter. Conclusion3D echocardiographic assessment of TA helps better understand its geometry and dynamism in functional TR, and is more accurate than 2D measurements for RV function assessment.

Detailed cellular and spatial characterization of chronic lung allograft dysfunction using imaging mass cytometry

Long-term survival after lung transplantation remains limited by chronic lung allograft dysfunction (CLAD), with two main phenotypes: bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS). We aimed to assess CLAD lung allografts using imaging mass cytometry (IMC), a high dimensional tissue imaging system allowing a multiparametric in situ exploration at a single cell level. 4 BOS, 4 RAS, and 4 control lung samples were stained with 35 heavy metal-tagged antibodies selected to assess structural and immune proteins of interest. We identified 50 immune and non-immune cell clusters. CLAD lungs had significantly reduced club cells. A Ki67-high basal cell population was mostly present in RAS and in proximity to memory T cells. Memory CD8+ T cells were more frequent in CLAD lungs, regulatory T cells more prominent in RAS. IMC is a powerful technology for detailed cellular analysis within intact organ structures that may shed further light on CLAD mechanisms.

Preventing and treating neurotrophic keratopathy by a single intrastromal injection of AAV-mediated gene therapy

PurposeNeurotrophic keratopathy (NK) is a degenerative corneal condition resulting from corneal nerve injury. Current therapies, including the recombinant human nerve growth factor (rhNGF) therapy, requires continuous administration. This study aims to develop a novel and highly effective gene therapy strategy for the prevention and treatment of NK. MethodsAdeno-associated virus (AAV) was transduced into corneal stromal cells by intrastromal injection. Three dimensional corneal wholemount imaging with co-immunostaining of ZO-1 and tubulin was utilized to assess the transduction of AAV.rh10. The efficacy of prevention and treatment of NK by a single intrastromal injection of AAV-Ngf was tested using capsaicin mouse model, herpes simplex keratitis (HSK) model, type Ⅱ diabetes model and alkali burn model. rhNGF eye drops served as the positive control. ResultsIntrastromal injection of AAV.rh10 efficiently transduced the subepithelial nerve plexus and retrogradely transported to the trigeminal ganglion (TG). A single injection of AAV.rh10-Ngf can significantly promote corneal nerve repair, accelerate corneal epithelial repair, reduce corneal stromal edema, and improve corneal sensitivity across the four NK models. The therapeutic effects were consistent with those achieved by continuous administration of rhNGF drops by 6 times daily. ConclusionsThis proof-of-concept study demonstrates that AAV.rh10-Ngf gene therapy is a promising method for preventing and treating of NK. Our results underline the potential for developing clinical trials to further explore the safety and efficacy of such gene therapy.

Exploring the Impact of Hemoglobin on Cerebral Blood Flow in Arterial Territories and Surgical Outcomes: Potential Implications for Moyamoya Disease Treatment.

Changes in levels of hemoglobin would result in alterations of cerebral blood flow (CBF). However, the impact of hemoglobin on CBF in moyamoya disease (MMD) remains largely unknown. This study sought to determine whether CBF would be influenced by hemoglobin before surgical revascularization and to analyze the relationships between hemoglobin and CBF with clinical outcome after surgery in patients with MMD. We prospectively enrolled adult patients with MMD undergoing surgical revascularization between June 2020 and December 2022. Preoperative CBF was measured in the territories of anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA, respectively) using 3-dimensional pseudo-continuous arterial spin labeling magnetic resonance imaging. Clinical outcome at 1 year after surgery was evaluated using the modified Rankin Scale. A total of 60 patients with MMD were included, with 25% (n=15) experiencing unfavorable outcomes. Patients with MMD exhibited lower CBF (ACA: P=0.007; MCA: P<0.001; PCA: P=0.014), compared with healthy controls (n=40). Hemoglobin was negatively and significantly associated with CBF (ACA: β=-0.45, P<0.001; MCA: β=-0.38, P<0.001; PCA: β=-0.54, P<0.001). CBF rather than hemoglobin was significantly related with clinical outcome (ACA: P<0.001; MCA: P<0.001; PCA: P=0.001), and CBF showed high discrimination in predicting clinical outcome (ACA: area under the curve, 0.84; MCA: area under the curve, 0.84; PCA: area under the curve, 0.80). Our findings demonstrate that hemoglobin significantly influences CBF, and CBF has a high predictive value for clinical outcome in MMD. The optimal hemoglobin level before surgical revascularization should be further investigated.

Low visceral fat volume and hypoalbuminemia as prognostic markers in hospitalized patients with coronavirus disease 2019 during the Omicron variant epidemic

Background&AimsThe rate of severe cases of coronavirus disease 2019 (COVID-19) has decreased since the Omicron variant became epidemic. Visceral fat volume was a risk factor for COVID-19 severity with prior prevalent variants, but whether visceral fat volume remains a risk factor for the Omicron variant is unclear. We investigated the associations of clinical factors including visceral fat volume with severity and mortality among hospitalized patients with COVID-19 during the Omicron variant epidemic. MethodsThis was a single-center retrospective cohort study conducted at the Teikyo University Hospital in Japan. We included hospitalized patients with COVID-19 during the Omicron variant epidemic who underwent computed tomography of the abdomen. Clinical data were obtained from the medical records and visceral fat area (VFA) was measured using a 3-dimensional image analysis system volume analyzer. Severity was determined by the presence or absence of oxygen supplementation. ResultsAmong the 226 patients, 66 patients showed moderate severity and 29 patients were non-survivors. Hypoalbuminemia was associated with severity (odds ratio [OR] 3.93, 95% confidence interval [CI] 1.91–8.07; p=0.0002), and hypoalbuminemia (OR 8.38, 95%CI 2.37–29.58; p=0.0010) and low VFA (OR 3.40, 95%CI 1.15–10.06; p=0.027) were associated with mortality. Decision tree analysis showed that mortality rate in the hypoalbuminemia and low-VFA group (37.3%) was significantly higher than in other groups (p≤0.01). ConclusionsLow visceral fat volume and hypoalbuminemia were associated with mortality in hospitalized patients with COVID-19 during the Omicron variant epidemic. Classification by VFA and serum albumin may allow simple prediction of mortality risk among hospitalized patients with COVID-19.