Higher glucose and insulin responses to a mixed meal are associated with increased risk of diabetic retinopathy in Indigenous Americans.

PURPOSE: Prior research has focused on glucose/insulin responses to meal challenges to create personalized diets to improve health, though it is unclear if these responses predict chronic diseases. We aimed to identify glucose and insulin responses to a mixed meal tolerance test (MMTT) that predict the development of diabetic retinopathy (DR) and compare the predictive abilities with the oral glucose tolerance test (OGTT). METHODS: Indigenous American adults without diabetes (n = 168) underwent a 4-h MMTT, body composition assessment, and a 3-h OGTT at baseline. During follow-up (median 13.4 years), DR was diagnosed by direct ophthalmoscopy (n = 28) after onset of type 2 diabetes. Total and incremental area under the curve (AUC and iAUC) were calculated from glucose/insulin responses after the MMTT and OGTT. RESULTS: In separate Cox proportional hazards models adjusted for age, sex, and body fat (%), MMTT glucose AUCs (180-min and 240-min) and iAUC (180-min) predicted DR (HR 1.50, 95% CI 1.06, 2.12; HR 1.50, 95% CI 1.05, 2.14; HR 1.58, 95% CI 1.01, 2.46). The predictive abilities were better than the fasting OGTT glucose (p < 0.01) but similar to the 120-min OGTT glucose (p = 0.53). MMTT insulin AUCs (180-min and 240-min) and iAUC (180-min) also predicted DR (HR 1.65, 95% CI 1.09, 2.51; HR 1.58, 95% CI 1.00, 2.35; HR 1.53 95% CI 1.06, 2.22) while insulin AUC and iAUC from the OGTT did not (p > 0.05). CONCLUSIONS: Higher MMTT glucose and insulin responses predicted DR and were comparable to the OGTT, supporting the use of a meal challenge for precision nutrition. TRIAL REGISTRATIONS: Clinical Trial Registry: ClinicalTrials.gov identifier: NCT00340132, NCT00339482.

Scalable SoftGroup for 3D Instance Segmentation on Point Clouds.

This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping instance segmentation results. Unfortunately, errors stemming from hard decisions propagate into the grouping, resulting in poor overlap between predicted instances and ground truth and substantial false positives. To address the abovementioned problems, SoftGroup allows each point to be associated with multiple classes to mitigate the uncertainty stemming from semantic prediction. It also suppresses false positive instances by learning to categorize them as background. Regarding scalability, the existing fast methods require computational time on the order of tens of seconds on large-scale scenes, which is unsatisfactory and far from applicable for real-time. Our finding is that the k-Nearest Neighbor ( k-NN) module, which serves as the prerequisite of grouping, introduces a computational bottleneck. SoftGroup is extended to resolve this computational bottleneck, referred to as SoftGroup++. The proposed SoftGroup++ reduces time complexity with octree k-NN and reduces search space with class-aware pyramid scaling and late devoxelization. Experimental results on various indoor and outdoor datasets demonstrate the efficacy and generality of the proposed SoftGroup and SoftGroup++. Their performances surpass the best-performing baseline by a large margin (6%  âˆ¼  16%) in terms of AP 50. On datasets with large-scale scenes, SoftGroup++ achieves a 6× speed boost on average compared to SoftGroup. Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods.

Atomic Dynamics of Multi-Interfacial Migration and Transformations.

Redox-induced interconversions of metal oxidation states typically result in multiple phase boundaries that separate chemically and structurally distinct oxides and suboxides. Directly probing such multi-interfacial reactions is challenging because of the difficulty in simultaneously resolving the multiple reaction fronts at the atomic scale. Using the example of CuO reduction in H2 gas, a reaction pathway of CuO → monoclinic m-Cu4 O3 → Cu2 O is demonstrated and identifies interfacial reaction fronts at the atomic scale, where the Cu2 O/m-Cu4 O3 interface shows a diffuse-type interfacial transformation; while the lateral flow of interfacial ledges appears to control the m-Cu4 O3 /CuO transformation. Together with atomistic modeling, it is shown that such a multi-interface transformation results from the surface-reaction-induced formation of oxygen vacancies that diffuse into deeper atomic layers, thereby resulting in the formation of the lower oxides of Cu2 O and m-Cu4 O3 , and activate the interfacial transformations. These results demonstrate the lively dynamics at the reaction fronts of the multiple interfaces and have substantial implications for controlling the microstructure and interphase boundaries by coupling the interplay between the surface reaction dynamics and the resulting mass transport and phase evolution in the subsurface and bulk.

Observation of Sub-10 nm Transition Metal Dichalcogenide Nanocrystals in Rapidly Heated van der Waals Heterostructures.

Two-dimensional materials, such as transition metal dichalcogenides (TMDCs), have the potential to revolutionize the field of electronics and photonics due to their unique physical and structural properties. This research presents a novel method for synthesizing crystalline TMDCs crystals with <10 nm size using ultrafast migration of vacancies at elevated temperatures. Through in situ and ex situ processing and using atomic-level characterization techniques, we analyzed the shape, size, crystallinity, composition, and strain distribution of these nanocrystals. These nanocrystals exhibit electronic structure signatures that differ from the 2D bulk: i.e., uniform mono- and multilayers. Further, our in situ, vacuum-based synthesis technique allows observation and comparison of defect and phase evolution in these crystals formed under van der Waals heterostructure confinement versus unconfined conditions. Overall, this research demonstrates a solid-state route to synthesizing uniform nanocrystals of TMDCs and lays the foundation for materials science in confined 2D spaces under extreme conditions.

[Research progress on biomarkers and detection methods for Alzheimer's disease diagnosis in vitro].

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

SKIN REINNERVATION BY COLLATERAL SPROUTING FOLLOWING SPARED NERVE INJURY IN MICE.

Following peripheral nerve injury, denervated tissues can be reinnervated via regeneration of injured neurons or via collateral sprouting of neighboring uninjured afferents into the denervated territory. While there has been substantial focus on mechanisms underlying regeneration, collateral sprouting has received relatively less attention. In this study, we used immunohistochemistry and genetic neuronal labeling to define the subtype specificity of sprouting-mediated reinnervation of plantar hind paw skin in the mouse spared nerve injury (SNI) model, in which productive regeneration cannot occur. Following an initial loss of cutaneous afferents in the tibial nerve territory, we observed progressive centripetal reinnervation by multiple subtypes of neighboring uninjured fibers into denervated glabrous and hairy plantar skin. In addition to dermal reinnervation, CGRP-expressing peptidergic fibers slowly but continuously repopulated the denervated epidermis, Interestingly, GFRα2-expressing nonpeptidergic fibers exhibited a transient burst of epidermal reinnervation, followed by trend towards regression. Presumptive sympathetic nerve fibers also sprouted into the denervated territory, as did a population of myelinated TrkC lineage fibers, though the latter did so less efficiently. Conversely, rapidly adapting Aß fiber and C fiber low threshold mechanoreceptor (LTMR) subtypes failed to exhibit convincing collateral sprouting up to 8 weeks after nerve injury. Optogenetics and behavioral assays further demonstrated the functionality of collaterally sprouted fibers in hairy plantar skin with restoration of punctate mechanosensation without hypersensitivity. Our findings advance understanding of differential collateral sprouting among sensory neuron subpopulations and may guide strategies to promote the progression of sensory recovery or limit maladaptive sensory phenomena after peripheral nerve injury. Significance Statement: Following nerve injury, whereas one mechanism for tissue reinnervation is regeneration of injured neurons, another, less well studied mechanism is collateral sprouting of nearby uninjured neurons. In this study, we examined collateral sprouting in denervated mouse skin and showed that it involves some, but not all neuronal subtypes. Despite such heterogeneity, a significant degree of restoration of punctate mechanical sensitivity is achieved. These findings highlight the diversity of collateral sprouting among peripheral neuron subtypes and reveal important differences between pre- and post-denervation skin that might be appealing targets for therapeutic correction to enhance functional recovery from denervation and prevent unwanted sensory phenomena such as pain or numbness.

Clinic efficacy and safety of ultrasound-guided Mammotome-assisted surgery for patients with breast benign tumors.

OBJECTIVE: The aim of this study was to assess the efficacy of ultrasound-guided Mammotome-assisted resection vs. conventional open surgery for benign breast tumors. PATIENTS AND METHODS: From July 2019 to December 2020, 134 suitable patients with benign breast cancers treated at our institution (Breast Surgery Department) were recruited and randomly allocated (1:1) to receive either Mammotome-assisted tumor excision (observation group) or open surgery (control group). The primary endpoint was clinical effectiveness, with surgical outcomes, complications, and satisfaction as secondary endpoints. RESULTS: Mammotome-assisted surgery resulted in shorter operative time, scar length, and postoperative healing time and less intraoperative bleeding volume vs. open surgery (p<0.001). Mammotome-assisted surgery was associated with a significantly higher clinical efficacy vs. open surgery (p<0.05). Patients receiving Mammotome-assisted surgery had a lower incidence of complications vs. those given open surgery (p<0.05). A significantly higher satisfaction was observed in patients given Mammotome-assisted surgery vs. open surgery (p<0.05). CONCLUSIONS: In comparison to standard open surgery, ultrasound-guided Mammotome-assisted surgery provides a viable alternative for breast benign tumor removal with superior efficacy, shorter operating time, less trauma, higher safety, fewer complications, and higher patient satisfaction.

[The incidence of artificial stone-related silicosis among interior decoration workers in different countries].

In this paper, 177 cases of artificial stone-related silicosis in interior decoration workers from Israel, Spain, USA, Italy and Australia were analyzed. Interior decoration workers were from small businesses (or workshops), engaged in kitchen and/or bathroom artificial stone countertops cutting, grinding, polishing and other reprocessing. In the working environment, the content of crystalline silica in artificial stone was more than 70%, and the concentration of silica dust exceeded the relevant standards. Most workplaces used dry cutting without ventilation and dust removal and other dust-proof measures, and most workers did not wear qualified respiratory protective equipment. Taking comprehensive measures such as wet operation, ventilation and dust removal, and individual protection can effectively prevent the occurrence of artificial stone-related silicosis.

Clinical Validation of a Wearable Piezoelectric Blood-Pressure Sensor for Continuous Health Monitoring.

Wearable blood-pressure sensors have recently attracted attention as healthcare devices for continuous non-invasive arterial pressure (CNAP) monitoring. However, the accuracy of wearable blood-pressure (BP) monitoring devices has been controversial due to the low signal quality of sensors, the absence of an accurate transfer function to convert the sensor signals into BP values, and the lack of clinical validation regarding measurement precision. Here, a wearable piezoelectric blood-pressure sensor (WPBPS) is reported, which achieves a high normalized sensitivity (0.062 kPa-1 ), and fast response time (23 ms) for CNAP monitoring. The transfer function of a linear regression model is designed, offering a simple solution to convert the flexible piezoelectric sensor signals into BP values. In order to verify the measurement accuracy of WPBPS, clinical trials are performed on 35 subjects aged from 20 to 80 s after screening. The mean difference between the WPBPS and a commercial sphygmomanometer of 175 BP data pairs is -0.89 ± 6.19 and -0.32 ± 5.28 mmHg for systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. By building a WPBPS-embedded wristwatch, the potentially promising use of a convenient, portable, continuous BP monitoring system for cardiovascular disease diagnosis is demonstrated.

Efficient Convolutional Neural Networks for Semiconductor Wafer Bin Map Classification.

The results obtained in the wafer test process are expressed as a wafer map and contain important information indicating whether each chip on the wafer is functioning normally. The defect patterns shown on the wafer map provide information about the process and equipment in which the defect occurred, but automating pattern classification is difficult to apply to actual manufacturing sites unless processing speed and resource efficiency are supported. The purpose of this study was to classify these defect patterns with a small amount of resources and time. To this end, we explored an efficient convolutional neural network model that can incorporate three properties: (1) state-of-the-art performances, (2) less resource usage, and (3) faster processing time. In this study, we dealt with classifying nine types of frequently found defect patterns: center, donut, edge-location, edge-ring, location, random, scratch, near-full type, and None type using open dataset WM-811K. We compared classification performance, resource usage, and processing time using EfficientNetV2, ShuffleNetV2, MobileNetV2 and MobileNetV3, which are the smallest and latest light-weight convolutional neural network models. As a result, the MobileNetV3-based wafer map pattern classifier uses 7.5 times fewer parameters than ResNet, and the training speed is 7.2 times and the inference speed is 4.9 times faster, while the accuracy is 98% and the F1 score is 89.5%, achieving the same level. Therefore, it can be proved that it can be used as a wafer map classification model without high-performance hardware in an actual manufacturing system.

CE-BART: Cause-and-Effect BART for Visual Commonsense Generation.

"A Picture is worth a thousand words". Given an image, humans are able to deduce various cause-and-effect captions of past, current, and future events beyond the image. The task of visual commonsense generation has the aim of generating three cause-and-effect captions for a given image: (1) what needed to happen before, (2) what is the current intent, and (3) what will happen after. However, this task is challenging for machines, owing to two limitations: existing approaches (1) directly utilize conventional vision-language transformers to learn relationships between input modalities and (2) ignore relations among target cause-and-effect captions, but consider each caption independently. Herein, we propose Cause-and-Effect BART (CE-BART), which is based on (1) a structured graph reasoner that captures intra- and inter-modality relationships among visual and textual representations and (2) a cause-and-effect generator that generates cause-and-effect captions by considering the causal relations among inferences. We demonstrate the validity of CE-BART on the VisualCOMET and AVSD benchmarks. CE-BART achieved SOTA performance on both benchmarks, while an extensive ablation study and qualitative analysis demonstrated the performance gain and improved interpretability.

[Research progress on the treatment of presbyopia].

Presbyopia is a physiological aging situation that the plasticity and elasticity of the lens and the function of the ciliary muscle become weaker, resulting in a decreased accommodation and inability to focus on near objects. Nowadays, there are many clinical strategies to correct presbyopia, each of which has its own advantages and disadvantages, however, there is no true sense of way to restore accommodation function. This article reviews both worldwide and domestic research on presbyopia, and analyzes and summaries the status quo as well as research progress of presbyopia correction modalities, surgical approaches, and drug therapies, hoping to provide a reference for clinical works.

[Recent progress of aptasensors for tumor exosome-associated protein detection].

Exosomes are phospholipid bilayer membrane-enclosed vesicles released from cells with diameters of 30-150 nm, exosomes can directly reflect the physiological and functional state of secretory cells, participate in material transport and information communication between cells, which are of great significance as biomarkers for early tumor diagnosis and treatment evaluation. There are many detection methods for exosomes, among which aptasensor technology with the properties of low price and easy operation, fast response, high sensitivity, remarkable specificity helps tumor patients to find, diagnose and treat early, improve the survival rate, and provide important basis for the evaluation of the prognosis. There are seven types of common aptasensors: fluorescent, electrochemical, colorimetric, luminescence, lateral flow strips, surface-enhanced Raman scattering and surface plasmon resonance sensors. Different aptasensors have different characteristics, this article focuses on the research progress of several common aptasensor for tumor exosomes detection.

Dual-Scale Doppler Attention for Human Identification.

This paper considers a Deep Convolutional Neural Network (DCNN) with an attention mechanism referred to as Dual-Scale Doppler Attention (DSDA) for human identification given a micro-Doppler (MD) signature induced as input. The MD signature includes unique gait characteristics by different sized body parts moving, as arms and legs move rapidly, while the torso moves slowly. Each person is identified based on his/her unique gait characteristic in the MD signature. DSDA provides attention at different time-frequency resolutions to cater to different MD components composed of both fast-varying and steady. Through this, DSDA can capture the unique gait characteristic of each person used for human identification. We demonstrate the validity of DSDA on a recently published benchmark dataset, IDRad. The empirical results show that the proposed DSDA outperforms previous methods, using a qualitative analysis interpretability on MD signatures.

Visual Pretraining via Contrastive Predictive Model for Pixel-Based Reinforcement Learning.

In an attempt to overcome the limitations of reward-driven representation learning in vision-based reinforcement learning (RL), an unsupervised learning framework referred to as the visual pretraining via contrastive predictive model (VPCPM) is proposed to learn the representations detached from the policy learning. Our method enables the convolutional encoder to perceive the underlying dynamics through a pair of forward and inverse models under the supervision of the contrastive loss, thus resulting in better representations. In experiments with a diverse set of vision control tasks, by initializing the encoders with VPCPM, the performance of state-of-the-art vision-based RL algorithms is significantly boosted, with 44% and 10% improvement for RAD and DrQ at 100 steps, respectively. In comparison to the prior unsupervised methods, the performance of VPCPM matches or outperforms all the baselines. We further demonstrate that the learned representations successfully generalize to the new tasks that share a similar observation and action space.

[Thrombotic occlusion of the extracorporeal circulation during hepatic chemosaturation despite targeted anticoagulation : A case report]. / Thrombotischer Verschluss der extrakorporalen Zirkulation während hepatischer Chemosaturation trotz zielgerechter Antikoagulation : Ein Fallbericht.

Percutaneous hepatic chemosaturation is a treatment option for unresectable primary or secondary liver tumors. In this procedure the part of the inferior vena cava (VCI) that collects blood from the hepatic veins is isolated using a double balloon catheter. Like this, systemic distribution of the chemotherapeutic agent melphalan which is administered via the hepatic artery can be prevented. After passage through the liver and drainage from the retrohepatic VCI, the chemosaturated blood passes through two extracorporeal filters. Subsequently, the filtered blood is returned via the jugular vein. The procedure is often accompanied by severe hemodynamic instability, the cause of which is still not completely understood. In addition, coagulation management of extracorporeal circulation is often challenging. The authors report a case in which a thrombus formed in the returning leg of the extracorporeal circulation despite sufficient activated clotting time (ACT). Targeted problem search and resolution were necessary simultaneously to hemodynamic stabilization and interdisciplinary collaboration to successfully perform the intervention and provide the patient with safe treatment.

Pooled analysis of drug-related interstitial lung disease and/or pneumonitis in nine trastuzumab deruxtecan monotherapy studies.

INTRODUCTION: This pooled analysis of nine phase I and II trastuzumab deruxtecan (T-DXd) monotherapy studies described drug-related interstitial lung disease (ILD)/pneumonitis in patients treated with T-DXd. METHODS: Patients who received T-DXd across nine studies were included. Investigator-assessed ILD/pneumonitis events were retrospectively reviewed by an independent adjudication committee; events adjudicated as drug-related ILD/pneumonitis are summarized. RESULTS: The analysis included 1150 patients (breast cancer, 44.3%; gastric cancer, 25.6%; lung cancer, 17.7%; colorectal cancer, 9.3%; other cancer, 3.0%). Median treatment duration was 5.8 (range, 0.7-56.3) months, with a median of 4 (range, 1-27) prior lines of therapy. The overall incidence of adjudicated drug-related ILD/pneumonitis was 15.4% (grade 5, 2.2%). Most patients with ILD/pneumonitis experienced low-grade events (grade 1 or 2, 77.4%); 87.0% had their first event within 12 months [median, 5.4 (range, <0.1-46.8) months] of their first dose of T-DXd. Based on data review, adjudicated ILD/pneumonitis onset occurred earlier than identified by investigators for 53.2% of events [median difference in onset date, 43 (range, 1-499) days]. Stepwise Cox regression identified several baseline factors potentially associated with increased risk of adjudicated drug-related ILD/pneumonitis: age <65 years, enrollment in Japan, T-DXd dose >6.4 mg/kg, oxygen saturation <95%, moderate/severe renal impairment, presence of lung comorbidities, and time since initial diagnosis >4 years. CONCLUSIONS: In this pooled analysis of heavily treated patients, the incidence of ILD/pneumonitis was 15.4%, with most being low grade and occurring in the first 12 months of treatment. The benefit-risk of T-DXd treatment is positive; however, some patients may be at increased risk of developing ILD/pneumonitis, and further investigation is needed to confirm ILD/pneumonitis risk factors. Close monitoring and proactive management of ILD/pneumonitis are warranted for all.

Dragon 1 Protocol Manuscript: Training, Accreditation, Implementation and Safety Evaluation of Portal and Hepatic Vein Embolization (PVE/HVE) to Accelerate Future Liver Remnant (FLR) Hypertrophy.

STUDY PURPOSE: The DRAGON 1 trial aims to assess training, implementation, safety and feasibility of combined portal- and hepatic-vein embolization (PVE/HVE) to accelerate future liver remnant (FLR) hypertrophy in patients with borderline resectable colorectal cancer liver metastases. METHODS: The DRAGON 1 trial is a worldwide multicenter prospective single arm trial. The primary endpoint is a composite of the safety of PVE/HVE, 90-day mortality, and one year accrual monitoring of each participating center. Secondary endpoints include: feasibility of resection, the used PVE and HVE techniques, FLR-hypertrophy, liver function (subset of centers), overall survival, and disease-free survival. All complications after the PVE/HVE procedure are documented. Liver volumes will be measured at week 1 and if applicable at week 3 and 6 after PVE/HVE and follow-up visits will be held at 1, 3, 6, and 12 months after the resection. RESULTS: Not applicable. CONCLUSION: DRAGON 1 is a prospective trial to assess the safety and feasibility of PVE/HVE. Participating study centers will be trained, and procedures standardized using Work Instructions (WI) to prepare for the DRAGON 2 randomized controlled trial. Outcomes should reveal the accrual potential of centers, safety profile of combined PVE/HVE and the effect of FLR-hypertrophy induction by PVE/HVE in patients with CRLM and a small FLR. TRIAL REGISTRATION: Clinicaltrials.gov: NCT04272931 (February 17, 2020). Toestingonline.nl: NL71535.068.19 (September 20, 2019).

Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation.

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly and indirectly correlated with the sensitive attribute in a complex manner, simple disparate treatment is ineffective in reducing performance disparity. This paper focuses on achieving counterfactual fairness for facial attribute classification. Each labeled input image is used to generate two synthetic replicas: one under factual assumptions about the sensitive attribute and one under counterfactual. The proposed causal graph-based attribute translation generates realistic counterfactual images that consider the complicated causal relationship among the attributes with an encoder-decoder framework. A causal graph represents complex relationships among the attributes and is used to sample factual and counterfactual facial attributes of the given face image. The encoder-decoder architecture translates the given facial image to have sampled factual or counterfactual attributes while preserving its identity. The attribute classifier is trained for fair prediction with counterfactual regularization between factual and corresponding counterfactual translated images. Extensive experimental results on the CelebA dataset demonstrate the effectiveness and interpretability of the proposed learning method for classifying multiple face attributes.