Clinical evaluation of the effect for prophylactic balloon occlusion in pregnancies complicated with placenta accreta spectrum disorder: A systematic review and meta-analysis.

BACKGROUND: Placenta accreta spectrum (PAS) disorder is a critical and severe obstetric condition associated with high risk of intraoperative massive hemorrhage and cesarean hysterectomy. Severe obstetric hemorrhage is currently one of the leading causes of maternal death worldwide. Prophylactic balloon occlusions, including prophylactic balloon occlusion of the abdominal aorta (PBOAA) and prophylactic balloon occlusion of the internal iliac arteries (PBOIIA), are the most common means of controlling hemorrhage in patients with PAS disorder, but their effectiveness is still debated. OBJECTIVE: A systematic review and meta-analysis were conducted to evaluate the clinical effectiveness of prophylactic balloon occlusion during cesarean section (CS) in improving maternal outcomes for PAS patients. SEARCH STRATEGY: MEDLINE, EMBASE, OVID, PubMed and the Cochrane Library were systematically searched from the inception dates to June 2022, using the keywords "placenta accreta spectrum disorder/morbidly adherent placenta (placenta previa, placenta accreta, placenta increta, placenta percreta), balloon occlusion, internal iliac arteries, abdominal aorta, hemorrhage, hysterectomy, estimated blood loss (EBL), packed red blood cells (PRBCs)" to identify the systematic reviews or meta-analyses. SELECTION CRITERIA: All articles regarding PAS disorders and including the application of balloon occlusion were included in the screening. DATA COLLECTION AND ANALYSIS: Two independent researchers performed the data extraction and assessed study quality. EBL volume and PRBC transfusion volume was regarded as the primary endpoints. Random and fixed effects models were used for the meta-analysis (RRs and 95% CIs), and the Newcastle-Ottawa Scale was used for quality assessments. MAIN RESULTS: Of 429 studies identified, a total of 35 trials involving the application of balloon occlusion for patients with PAS disorder during CS were included. A total of 19 studies involving 935 patients who underwent PBOIIA were included in the PBOIIA group, and 851 patients were included in control 1 group. Ten studies including 428 patients with PAS who underwent PBOAA were allocated to the PBOAA group, and 324 patients without PBOAA were included in control 2 group. Simultaneously, we compared the effect on PBOAA and PBOIIA including seven studies, which referred to 267 cases in the PBOAA group and 313 cases in the PBOIIA group. The results showed that the PBOIIA group had a reduced EBL volume (MD: 342.06 mL, 95% CI: -509.90 to -174.23 mL, I2 = 77%, P < 0.0001) and PRBC volume (MD: -1.57 U, 95% CI: -2.49 to -0.66 U, I2 = 91%, P = 0.0008) than that in control 1 group. With regard to the EBL volume (MD: -926.42 mL, 95% CI: -1437.07 to -415.77 mL, I2 = 96%, P = 0.0004) and PRBC transfusion volume (MD: -2.42 U, 95% CI: -4.25 to -0.59 U, I2 = 99%, P = 0.009) we found significant differences between the PBOAA group and control 2 group. Prophylactic balloon occlusion (PBOAA and PBOIIA) had a significant effect on reducing intraoperative blood loss and blood transfusion volume in patients with PAS. Moreover, PBOAA was more effective than PBOIIA in reducing intraoperative blood loss (MD: -406.63 mL, 95% CI: -754.12 to -59.13 mL, I2 = 92%, P = 0.020), but no significant difference in controlling PRBCs (MD: -3.48 U, 95% CI: -8.90 to 1.95 U, I2 = 99%, P = 0.210) between the PBOIIA group and the PBOAA group. Hierarchical analysis was conducted by differentiating gestational weeks and maternal age to reduce the high heterogeneity of meta-analysis. Hierarchical analysis results demonstrated the heterogeneities of the study were reduced to some extent, and gestational weeks and maternal age might be the cause of increased heterogeneity. CONCLUSION: Prophylactic balloon occlusion is a safe and effective method to control hemorrhage and reduce PRBC transfusion volume for patients with PAS, and PBOAA could reduce more intraoperative blood loss than PBOIIA. However, we found no statistical difference in lessening packed red blood cell transfusion volume for PAS patients. Hence, preoperative prophylactic balloon occlusion is the recommended application for PAS patients in obstetric CSs. Furthermore, PBOAA is preferred for controlling intraoperative bleeding in patients with corresponding medical conditions.

Rapid Selection of Patients Suitable for Deep Inspiration Breath-Hold Using an Automatic Delineating System and RapidPlan Model in Left Breast Cancer Patients undergoing Adjuvant Radiotherapy with IMRT.

PURPOSE: This study aimed to determine whether radiotherapy plans created using an automatic delineating system and a RapidPlan (RP) module could rapidly and accurately predict heart doses and benefit from deep inspiratory breath-hold (DIBH)in left breast cancer patients. METHODS AND MATERIALS: One hundred thirty-six clinically approved free breathing (FB) plans for patients with left breast cancer were included, defined as manual delineation-manual plan (MD-MP). A total of 104/136 plans were selected for RP model training. A total of 32/136 patients were automatically delineated by software, after which the RP generated plans, defined as automatic delineation-RapidPlan (AD-RP). In addition, 40 patients who used DIBH were included to analyze differences in heart benefits from DIBH. RESULTS: Two RP models were established for post breast-conserving surgery (BCS) and post modified radical mastectomy (MRM). There were no significant differences in most of the dosimetric parameters between the MD-MP and AD-RP. The heart doses of the two plans were strongly correlated in patients after BCS (0.80 ≤ r ≤ 0.88, P < 0.05) and moderately correlated in patients after MRM (0.46 ≤ r ≤ 0.58, P < 0.05). The RP model predicted the mean heart dose (MHD) within ± 59.67 cGy and ± 63.32 cGy for patients who underwent the two surgeries described above. The heart benefits from DIBH were significantly greater in patients with FB-MHD ≥ 4 Gy than in those with FB-MHD < 4 Gy. CONCLUSIONS: The combined automatic delineation RP model allows for the rapid and accurate prediction of heart dose under FB in patients with left breast cancer. FB-MHD ≥ 4 Gy can be used as a dose threshold to select patients suitable for DIBH.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning.

BACKGROUND: Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. METHODS: IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. RESULTS: Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. CONCLUSIONS: This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Selective Activation of Lattice Oxygen Site Through Coordination Engineering to Boost the Activity and Stability of Oxygen Evolution Reaction.

Although Ru-based materials are among the outstanding catalysts for the oxygen evolution reaction (OER), the instability issue still haunts them and impedes the widespread application. The instability of Ru-based OER catalysts is generally ascribed to the formation of soluble species through the over-oxidation of Ru and structural decomposition caused by involvement of lattice oxygen. Herein, an effective strategy of selectively activating the lattice oxygen around Ru site is proposed to improve the OER activity and stability. Our synthesized spinel-type electrocatalyst of Ru and Zn co-doped Co3O4 showed an ultralow overpotential of 172 mV at 10 mA cm-2 and a long-term stability reaching to 100 hours at 10 mA cm-2 for alkaline OER. The experimental results and theoretical simulations demonstrated that the lattice oxygen site jointly connected with the octahedral Ru and tetrahedral Zn atoms became more active than other oxygen sites near Ru atom, which further lowered the reaction energy barriers and avoided generating excessive oxygen vacancies to enhance the structural stability of Ru sites. The findings hope to provide a new perspective to improve the catalytic activity of Ru-incorporated OER catalysts and the stability of lattice-oxygen-mediated mechanism.

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals.

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the previous active learning (e.g., AADA and MHPL) and UDA (e.g., Tent and CPR) methods, and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research (Git).

The impact of maternal intrahepatic cholestasis during pregnancy on the growth trajectory of offspring: a population-based nested case‒control cohort study.

BACKGROUND: Intrahepatic cholestasis of pregnancy (ICP) is associated with an increased risk of adverse fetal outcomes, yet its influence on offspring growth remains unclear. Our study dynamically tracks growth rates in children from ICP and healthy mothers and investigates the link between maternal liver function and developmental abnormalities in offspring. METHOD: Our case‒control study involved 97 women with ICP and 152 with uncomplicated pregnancies nested in a cohort of their offspring, including 50 from the ICP group and 87 from the uncomplicated pregnancy group. We collected pediatric growth and development data, with a maximum follow-up duration of 36 months. Stratified analyses of children's height, weight, and head circumference were conducted, and Spearman's rank correlation was applied to examine the relationships between maternal serological markers and pediatric growth metrics. RESULT: Maternal liver and renal functions, along with serum lipid profiles, significantly differed between the ICP and normal groups. In the ICP group, the offspring showed elevated alanine aminotransferase (ALT), direct bilirubin (DBIT), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (APOB) levels. Notably, the length-for-age z score (LAZ), weight-for-age z score (WAZ), and head circumference-for-age z score (HCZ) were lower in ICP offspring compared with those from normal pregnancies within the 1- to 12-month age range (P < 0.05). However, no significant differences in LAZ, weight-for-length z score (WLZ), BMI-for-age z score (BAZ), or HCZ were observed between groups in the 13- to 36-month age range. Maternal maximum lactate dehydrogenase (LDH) and total bile acids (TBA) levels during pregnancy were inversely correlated with LAZ and WAZ in the first year. Furthermore, offspring of mothers with ICP exhibited a greater incidence of stunting (24% vs. 6.9%, P = 0.004) and abnormal HCZ (14% vs. 3.7%, P = 0.034). CONCLUSIONS: Growth disparities in offspring of ICP-affected pregnancies were most significant within the 1- to 12-month age range. During this period, maximum maternal LDH and TBA levels were negatively correlated with LAZ and WAZ values of offspring. The observation of similar growth rates between ICP and control group offspring from 13 to 36 months suggested catch-up growth in the ICP group.

Multimodal PA/US imaging in Rheumatoid Arthritis: Enhanced correlation with clinical scores.

Background: Accurate assessment of Rheumatoid Arthritis (RA) activity remains a challenge. Multimodal photoacoustic/ultrasound (PA/US) joint imaging emerges as a novel imaging modality capable of depicting microvascularization and oxygenation levels in inflamed joints associated with RA. However, the scarcity of large-scale studies limits the exploration of correlating joint oxygenation status with disease activity. Objective: This study aimed to explore the correlation between multimodal PA/US imaging scores and RA disease activity, assessing its clinical applicability in managing RA. Methods: In this study, we recruited 111 patients diagnosed with RA and conducted examinations of seven small joints on their clinically dominant side using a PA/US imaging system. The PA and power Doppler ultrasound (PDUS) signals were semi-quantitatively assessed using a 0-3 grading system. The cumulative scores for PA and PDUS across these seven joints (PA-sum and PDUS-sum) were calculated. Relative oxygen saturation (So2) values of inflamed joints on the clinically dominant side were measured, and categorized into four distinct PA+So2 patterns. The correlation between PA/US imaging scores and disease activity indices was systematically evaluated. Results: Analysis of 777 small joints in 111 patients revealed that the PA-sum scores exhibited a strong positive correlation with standard clinical scores for RA, including DAS28 [ESR] (ρ = 0.682), DAS28 [CRP] (ρ = 0.683), CDAI (ρ = 0.738), and SDAI (ρ = 0.739), all with p < 0.001. These correlations were superior to those of the PDUS-sum scores (DAS28 [ESR] ρ = 0.559, DAS28 [CRP] ρ = 0.555, CDAI ρ = 0.575, SDAI ρ = 0.581, p < 0.001). Significantly, in patients with higher PA-sum scores, notable differences were observed in the erythrocyte sedimentation rate (ESR) (p < 0.01) and swollen joint count 28 (SJC28) (p < 0.01) between hypoxia and intermediate groups. Notably, RA patients in the hypoxia group exhibited higher clinical scores in certain clinical indices. Conclusion: Multi-modal PA/US imaging introduces potential advancements in RA assessment, especially regarding So2 evaluations in synovial tissues and associated PA scores. However, further studies are warranted, particularly with more substantial sample sizes and in multi-center settings. Summary: This study utilized multi-modal PA/US imaging to analyze Rheumatoid Arthritis (RA) patients' synovial tissues and affected joints. When juxtaposed with traditional PDUS imaging, the PA approach demonstrated enhanced sensitivity, especially concerning detecting small vessels in thickened synovium and inflamed tendon sheaths. Furthermore, correlations between the derived PA scores, PA+So2 patterns, and standard clinical RA scores were observed. These findings suggest that multi-modal PA/US imaging could be a valuable tool in the comprehensive assessment of RA, offering insights not only into disease activity but also into the oxygenation status of synovial tissues. However, as promising as these results are, further investigations, especially in larger and diverse patient populations, are imperative. Key points: ⸸ Multi-modal PA/US Imaging in RA: This novel technique was used to assess the So2 values in synovial tissues and determine PA scores of affected RA joints.⸸ Correlation significantly with Clinical RA Scores: Correlations significantly were noted between PA scores, PA+So2 patterns, and standard clinical RA metrics, hinting at the potential clinical applicability of the technique.

Development and application of an RNA nanostructure to induce transient RNAi in difficult transgenic plants.

The development of RNA interference (RNAi) is crucial for studying plant gene function. Its use, is limited to a few plants with well-established transgenic techniques. Spray-induced gene silencing (SIGS) introduces exogenous double-stranded RNA (dsRNA) into plants by spraying, injection, or irrigation, triggering the RNAi pathway to instantly silence target genes. As is a transient RNAi technology that does not rely on transgenic methods, SIGS has significant potential for studying gene function in plants lacking advanced transgenic technology. In this study, to enhance their stability and delivery efficiency, siRNAs were used as structural motifs to construct RNA nanoparticles (NPs) of four shapes: triangle, square, pentagon, and hexagon. These NPs, when synthesized by Escherichia coli, showed that triangular and square shapes accumulated more efficiently than pentagon and hexagon shapes. Bioassays revealed that RNA squares had the highest RNAi efficiency, followed by RNA triangles, with GFP-dsRNA showing the lowest efficiency at 4 and 7 days post-spray. We further explored the use of RNA squares in inducing transient RNAi in plants that are difficult to transform genetically. The results indicated that Panax notoginseng-derived MYB2 (PnMYB2) and Camellia oleifera-derived GUT (CoGUT) were significantly suppressed in P. notoginseng and C. oleifera, respectively, following the application of PnMYB2- and CoGUT-specific RNA squares. These findings suggest that RNA squares are highly effective in SIGS and can be utilized for gene function research in plants.

Effect of In Situ Heating on the Growth and Electrochromic Properties of Tungsten Trioxide Thin Films.

Electrochromism has emerged as a pivotal technology in the pursuit of energy efficiency and environmental sustainability, spurring significant research efforts aimed at the creation of advanced electrochromic devices. Most electrochromic materials are used for smart window applications. However, current electrochromic materials have been applied to new energy vehicles, cell phone back covers, AR glasses, and so on. More application scenarios put forward more requirements for the color of the colored states. Choosing the right color change in the application will be the trend in the future. In this work, tungsten trioxide (WO3) thin films were prepared by adjusting the in situ heating temperature. WO3 with a crystalline structure showed excellent cyclic stability (5000 cycles), electrochromic performance (ΔT = 77.7% at 633 nm, CE = 37.1 cm2/C), relatively fast bleaching/coloring speed (20.0 s/19.4 s), and the darkest coloring effect (L* = 29.32, a* = 7.41, b* = -22.12 for the colored state). These findings offer valuable insights into the manipulation of smart materials and devices, contributing to the advancement of electrochromic technology.

JIMR: Joint Semantic and Geometry Learning for Point Scene Instance Mesh Reconstruction.

Point scene instance mesh reconstruction is a challenging task since it requires both scene-level instance segmentation and instance-level mesh reconstruction from partial observations simultaneously. Previous works either adopt a detection backbone or a segmentation one, and then directly employ a mesh reconstruction network to produce complete meshes from incomplete instance point clouds. To further boost the mesh reconstruction quality with both local details and global smoothness, in this work, we propose JIMR, a joint framework with two cascaded stages for semantic and geometry understanding. In the first stage, we propose to perform both instance segmentation and object detection simultaneously. By making both tasks promote each other, this design facilitates subsequent mesh reconstruction by providing more precisely-segmented instance points and better alignment benefiting from predicted complete bounding boxes. In the second stage, we propose a complete-then-reconstruct procedure, where the completion module explicitly disentangles completion from reconstruction, and enables the usage of pre-trained weights of existing powerful completion and reconstruction networks. Moreover, we propose a comprehensive confidence score to filter proposals considering the quality of instance segmentation, bounding box detection, semantic classification, and mesh reconstruction at the same time. Experiments show that our proposed JIMR outperforms state-of-the-art methods regarding instance reconstruction qualitatively and quantitatively.

A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

OBJECTIVES: Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS: Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance. RESULTS: On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively. CONCLUSIONS: Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.

Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

A Video-based Automated Tracking and Analysis System of Plaque Burden in Carotid Artery Using Deep Learning: A Comparison with Senior Sonographers.

BACKGROUND AND OBJECTIVE: The incidence of stroke is rising, and it is the second major cause of mortality and the third leading cause of disability around the globe. The goal of this study was to rapidly and accurately identify carotid plaques and automatically quantify plaque burden using our automated tracking and segmentation US-video system. METHODS: We collected 88 common carotid artery transection videos (11048 frames) with a history of atherosclerosis or risk factors for atherosclerosis, which were randomly divided into training, test, and validation sets using a 6:3:1 ratio. We first trained different segmentation models to segment the carotid intima and adventitia, and calculate the maximum plaque burden automatically. Finally, we statistically analyzed the plaque burden calculated automatically by the best model and the results of manual labeling by senior sonographers. RESULTS: Of the three Artificial Intelligence (AI) models, the Robust Video Matting (RVM) segmentation model's carotid intima and adventitia Dice Coefficients (DC) were the highest, reaching 0.93 and 0.95, respectively. Moreover, the RVM model has shown the strongest correlation coefficient (0.61±0.28) with senior sonographers, and the diagnostic effectiveness between the RVM model and experts was comparable with paired-t test and Bland-Altman analysis [P= 0.632 and ICC 0.01 (95% CI: -0.24~0.27), respectively]. CONCLUSION: Our findings have indicated that the RVM model can be used in ultrasound carotid video. The RVM model can automatically segment and quantify atherosclerotic plaque burden at the same diagnostic level as senior sonographers. The application of AI to carotid videos offers more precise and effective methods to evaluate carotid atherosclerosis in clinical practice.

Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics.

Background: The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye. Purpose: This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy. Materials and methods: From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3-5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features. Results: The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules. Conclusion: This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.

Selective Cleavage of α-Olefins to Produce Acetylene and Hydrogen.

Acetylene production from mixed α-olefins emerges as a potentially green and energy-efficient approach with significant scientific value in the selective cleavage of C-C bonds. On the Pd(100) surface, it is experimentally revealed that C2 to C4 α-olefins undergo selective thermal cleavage to form surface acetylene and hydrogen. The high selectivity toward acetylene is attributed to the 4-fold hollow sites which are adept at severing the terminal double bonds in α-olefins to produce acetylene. A challenge arises, however, because acetylene tends to stay at the Pd(100) surface. By using the surface alloying methodology with alien Au, the surface Pd d-band center has been successfully shifted away from the Fermi level to release surface-generated acetylene from α-olefins as a gaseous product. Our study actually provides a technological strategy to economically produce acetylene and hydrogen from α-olefins.

Photoacoustic Imaging Radiomics to Identify Breast Cancer in BI-RADS 4 or 5 Lesions.

OBJECTIVES: To develop a nomogram based on photoacoustic imaging (PAI) radiomics and BI-RADs to identify breast cancer (BC) in BI-RADS 4 or 5 lesions detected by ultrasound (US). METHODS: In this retrospective study, 119 females with 119 breast lesions at US and PAI examination were included (January 2022 to December 2022). Patients were divided into the training set (n = 83) or testing set (n = 36) to develop a nomogram to identify BC in BI-RADS 4 or 5 lesions. Relevant factors at clinic, BI-RADS category, and PAI were reviewed. Univariate and multivariate regression was used to evaluate factors for associations with BC. To evaluate the diagnostic performance of nomogram, the area under the curve (AUC) of receiver operating characteristic curve, accuracy, specificity and sensitivity was employed. RESULTS: The nomogram that included BI-RADS category and PAI radiomics score demonstrated a high AUC of 0.925 (95%CI: 0.8467-0.9712) in the training set and 0.926 (95%CI: 0.846-1.000) in the test set. The nomogram also showed significantly better discrimination than the radiomics score (P = .048) or BI-RADS category (P = .009) in the training set. These significant differences were demonstrated in the testing set, outperform the radiomics score (P = .038) and BI-RADS category (P = .013). CONCLUSIONS: The nomogram developed with BI-RADS and PAI radiomics score can effectively identify BC in BI-RADS 4 or 5 lesions. This technique has the potential to further improve early diagnostic accuracy for BC.

Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer.

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.

Educational attainment, income, and attention deficit hyperactivity disorder: A mediation analysis based on two-step Mendelian randomization.

Previous studies have reported the relationship between educational attainment and attention deficit hyperactivity disorder (ADHD). However, the mechanism of this relationship remains unknown. It is well known that educational attainment correlates with income. Therefore, based on summary data from a genome-wide association study we used two-step Mendelian randomization (MR) to explore the role of income between education and ADHD. The inverse variance weighted (IVW) method was used in our analysis. The IVW results suggested that educational attainment and income were protective factors against ADHD. Educational attainment affects ADHD through income [ADHD: Beta = -0.68, 95% confidence interval (CI) = -0.87, -0.49; female: Beta = -0.87, 95% CI = -1.28, -0.47; male: Beta = -1.01, 95% CI = -1.34, -0.68; childhood: Beta = -0.52, 95% CI = -0.74, -0.30; late-diagnosed: Beta = -0.78, 95% CI = -1.11, -0.47; persistent: Beta = -0.82, 95% CI = -1.33, -0.31]. Income also affected ADHD through educational attainment [female: Beta = -1.08, 95% CI = -1.35, -0.83; male: Beta = -1.16, 95% CI = -1.57, -0.77; persistent: Beta = -1.48, 95% CI = -2.09, -0.94]. In the final analysis, data with heterogeneity were analyzed using IVW random effects results. The mechanism is that income will mediate the relationship between educational attainment and ADHD.

Diagnostic Performance of Deep Learning in Video-Based Ultrasonography for Breast Cancer: A Retrospective Multicentre Study.

OBJECTIVE: Although ultrasound is a common tool for breast cancer screening, its accuracy is often operator-dependent. In this study, we proposed a new automated deep-learning framework that extracts video-based ultrasound data for breast cancer screening. METHODS: Our framework incorporates DenseNet121, MobileNet, and Xception as backbones for both video- and image-based models. We used data from 3907 patients to train and evaluate the models, which were tested using video- and image-based methods, as well as reader studies with human experts. RESULTS: This study evaluated 3907 female patients aged 22 to 86 years. The results indicated that the MobileNet video model achieved an AUROC of 0.961 in prospective data testing, surpassing the DenseNet121 video model. In real-world data testing, it demonstrated an accuracy of 92.59%, outperforming both the DenseNet121 and Xception video models, and exceeding the 76.00% to 85.60% accuracy range of human experts. Additionally, the MobileNet video model exceeded the performance of image models and other video models across all evaluation metrics, including accuracy, sensitivity, specificity, F1 score, and AUC. Its exceptional performance, particularly suitable for resource-limited clinical settings, demonstrates its potential for clinical application in breast cancer screening. CONCLUSIONS: The level of expertise reached by the video models was greater than that achieved by image-based models. We have developed an artificial intelligence framework based on videos that may be able to aid breast cancer diagnosis and alleviate the shortage of experienced experts.

Donor-recipient intermicrobial interactions impact transfer of subspecies and fecal microbiota transplantation outcome.

Studies on fecal microbiota transplantation (FMT) have reported inconsistent connections between clinical outcomes and donor strain engraftment. Analyses of subspecies-level crosstalk and its influences on lineage transfer in metagenomic FMT datasets have proved challenging, as single-nucleotide polymorphisms (SNPs) are generally not linked and are often absent. Here, we utilized species genome bin (SGB), which employs co-abundance binning, to investigate subspecies-level microbiome dynamics in patients with autism spectrum disorder (ASD) who had gastrointestinal comorbidities and underwent encapsulated FMT (Chinese Clinical Trial: 2100043906). We found that interactions between donor and recipient microbes, which were overwhelmingly phylogenetically divergent, were important for subspecies transfer and positive clinical outcomes. Additionally, a donor-recipient SGB match was indicative of a high likelihood of strain transfer. Importantly, these ecodynamics were shared across FMT datasets encompassing multiple diseases. Collectively, these findings provide detailed insight into specific microbial interactions and dynamics that determine FMT success.