Trends in colorectal cancer screening compliance and incidence among 60- to 74-year-olds in China.

BACKGROUND: Compliance with colonoscopy among elderly individuals participating in colorectal cancer (CRC) screening programs is unsatisfactory, despite a high detection rate of bowel-related diseases. In this study, our aim was to analyze the impact of risk factors on the trends of compliance and detection rates in colonoscopy among high-risk individuals aged 60-74. METHODS: A retrospective study was conducted on the high-risk individuals aged 60-74 participating in the 2021 CRC screening program in Tianjin, China. Logistic regression analyses, including both univariate and multivariate analyses, were performed to explore the impact of different risk factors on colonoscopy compliance among the high-risk individuals. Besides, the study investigated the influence of various risk factors on the detection rates of bowel-related diseases among the high-risk individuals who underwent colonoscopy. RESULTS: A total of 24,064 high-risk individuals were included, and 5478 individuals received a free colonoscopy, with an overall compliance of 22.76%. Among them, the adenoma detection rate was 55.46%. Males and individuals with a positive FIT had high compliance and detection rates for CRC, advanced adenomas (AA), advanced colorectal neoplasia (ACN), and colorectal neoplasm (CN). Individuals aged 70-74 were associated with low compliance but high CRC, ACN, and CN detection rates. Individuals who reported a history of chronic constipation, bloody mucous, and CRC in first-degree relative showed high compliance but no significantwere associated with the detection rates of CRC, AA, and CN. CONCLUSION: This study reported several risk factors associated with the screening behaviors for CRC. Patterns and trends in CRC, AA, ACN, and CN compliance and detection rates correlate with risk factors.

An N-Shaped Lightweight Network with a Feature Pyramid and Hybrid Attention for Brain Tumor Segmentation.

Brain tumor segmentation using neural networks presents challenges in accurately capturing diverse tumor shapes and sizes while maintaining real-time performance. Additionally, addressing class imbalance is crucial for achieving accurate clinical results. To tackle these issues, this study proposes a novel N-shaped lightweight network that combines multiple feature pyramid paths and U-Net architectures. Furthermore, we ingeniously integrate hybrid attention mechanisms into various locations of depth-wise separable convolution module to improve efficiency, with channel attention found to be the most effective for skip connections in the proposed network. Moreover, we introduce a combination loss function that incorporates a newly designed weighted cross-entropy loss and dice loss to effectively tackle the issue of class imbalance. Extensive experiments are conducted on four publicly available datasets, i.e., UCSF-PDGM, BraTS 2021, BraTS 2019, and MSD Task 01 to evaluate the performance of different methods. The results demonstrate that the proposed network achieves superior segmentation accuracy compared to state-of-the-art methods. The proposed network not only improves the overall segmentation performance but also provides a favorable computational efficiency, making it a promising approach for clinical applications.

Prevalence and risk factors of colorectal neoplasia in individuals aged 40-49 years: Findings from screening program in China.

BACKGROUND AND AIM: The incidence of colorectal cancer (CRC) in individuals under 50 is increasing worldwide. We conducted an analysis of colonoscopy findings in high-risk individuals under 50 in the CRC screening program in Tianjin, China, to determine the detection rate and risk factors of advanced adenomas (AA), advanced colorectal neoplasia (ACN), colorectal neoplasia (CN). METHODS: Our study investigated individuals aged 40-49 who underwent CRC screening and completed colonoscopy, 2012-2020, while the 50-54 age group served as a control. We compared the detection rates of AA, ACN, and CN among three age groups using univariate and multivariable logistic regression analyses, and investigated the risk factors associated with AA, ACN, and CN among individuals aged 40-49. RESULTS: We found a gradual increase in the detection rate of AA, ACN, and CN among individuals aged 40-54. The detection rates for AA (OR 0.58; 95% CI 0.41-0.81), ACN (OR 0.58; 95% CI 0.43-0.77), and CN (OR 0.64; 95% CI 0.56-0.74) were lower in individuals aged 40-44 compared to 45-49. The detection rates of AA (OR 1.08; 95% CI 0.87-1.34) and ACN (OR 1.12; 95% CI 0.93-1.35) in individuals aged 45-49 were comparable with 50-54. Besides, lifestyle factors, BMI, and FIT are not associated with the detection rates of AA, ACN, and CN among individuals aged 40-49. CONCLUSIONS: Our study reveals screening data in individuals under 50, indicating comparable detection rates of AA and ACN in individuals aged 45-49 and 50-54. These findings provide valuable data support for optimizing the optimal age to initiate screening.

Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector.

Multi-robot cooperative object transport on uneven roads is challenging. The key barrier is dealing with nonholonomic and rigid-formation motion constraints. In this study, to alleviate the influence of these constraints on a multi-robot cooperative transport system (MRCTS), a six degree-of-freedom connector capable of sensing three-axial displacements, three-axial forces, and three-axial angular displacements is designed and employed. Based on the local displacements derived from each connector, we develop a position calibration method to calculate the relative position of each robot and achieve a centralized control strategy. Based on the forces sensed by each connector, we design a decentralized control strategy to accomplish cooperative transport in which a leader robot guides the follower robots toward a destination by applying forces, instead of centralized information broadcasting. The experimental results show that the MRCTS works well on an uneven surface, and the tracking errors are within the design stroke of the connectors, demonstrating the effectiveness of the design and control methods of the MRCTS.

Is Additive Manufacturing an Environmentally and Economically Preferred Alternative for Mass Production?

The manufacturing sector accounts for a large percentage of global energy use and greenhouse gas emissions, and there is growing interest in the potential of additive manufacturing (AM) to reduce the sector's environmental impacts. Across multiple industries, AM has been used to reduce material use in final parts by 35-80%, and recent publications have predicted that AM will enable the fabrication of customized products locally and on-demand, reducing shipping and material waste. In many contexts, however, AM is not a viable alternative to traditional manufacturing methods due to its high production costs. And in high-volume mass production, AM can lead to increased energy use and material waste, worsening environmental impacts compared to traditional production methods. Whether AM is an environmentally and economically preferred alternative to traditional manufacturing depends on several hidden aspects of AM that are not readily apparent when comparing final products, including energy-intensive and expensive material feedstocks, excessive material waste during production, high machine costs, and slow rates of production. We systematically review comparative studies of the environmental impacts and costs of AM in contrast with traditional manufacturing methods and identify the conditions under which AM is the environmentally and economically preferred alternative. We find that AM has lower production costs and environmental impacts when production volumes are relatively low (below ∼1,000 per year for environmental impacts and below 42-87,000 per year for costs, depending on the AM process and part geometry) or the parts are small and would have high material waste if traditionally manufactured. In cases when the geometric freedom of AM enables performance improvements that reduce environmental impacts and costs during a product's use phase, these can counteract the higher production impacts of AM, making it the preferred alternative at larger production volumes. AM's ability to be environmentally and economically beneficial for mass manufacturing in a wider variety of contexts is dependent on reducing the cost and energy intensity of material feedstock production, eliminating the need for support structures, raising production speeds, and reducing per unit machine costs. These challenges are not primarily caused by economies of scale, and therefore, they are not likely to be addressed by the increasing expansion of the AM sector. Instead, they will require fundamental advances in material science, AM production technologies, and computer-aided design software.

PneuNet: deep learning for COVID-19 pneumonia diagnosis on chest X-ray image analysis using Vision Transformer.

A long-standing challenge in pneumonia diagnosis is recognizing the pathological lung texture, especially the ground-glass appearance pathological texture. One main difficulty lies in precisely extracting and recognizing the pathological features. The patients, especially those with mild symptoms, show very little difference in lung texture, neither conventional computer vision methods nor convolutional neural networks perform well on pneumonia diagnosis based on chest X-ray (CXR) images. In the meanwhile, the Coronavirus Disease 2019 (COVID-19) pandemic continues wreaking havoc around the world, where quick and accurate diagnosis backed by CXR images is in high demand. Rather than simply recognizing the patterns, extracting feature maps from the original CXR image is what we need in the classification process. Thus, we propose a Vision Transformer (VIT)-based model called PneuNet to make an accurate diagnosis backed by channel-based attention through X-ray images of the lung, where multi-head attention is applied on channel patches rather than feature patches. The techniques presented in this paper are oriented toward the medical application of deep neural networks and VIT. Extensive experiment results show that our method can reach 94.96% accuracy in the three-categories classification problem on the test set, which outperforms previous deep learning models.

Quantitative evaluation of precautions against the COVID-19 indoor transmission through human coughing.

In this work, we focus on the dispersion of COVID-19-laden droplets using the transient computational fluid dynamics (CFD) modeling and simulation of the coughing process of virus carriers in an enclosure room, aiming to set up the basic prototype of popular precautionary strategies, i.e., face mask, upward ventilation, protective screen, or any combination thereof, against the indoor transmission of COVID-19 and other highly contagious diseases in the future. A multi-component Eulerian-Lagrangian CFD particle-tracking model with user-defined functions is utilized under 8 cases to examine the characteristics of droplet dispersion concerning the mass and heat transfer, droplet evaporation, air buoyancy, air convection, air-droplet friction, and turbulent dispersion. The result shows that implementing upward ventilation is the most effective measure, followed by wearing face masks. Protective screens can restrict the movement of the coughing droplets (though it will not reduce viral load). However, applying protective screens arranged with lean can be counterproductive in preventing the spread of COVID-19 when it is inappropriately placed with ventilation. The soundest solution is the combination of the face mask and upward ventilation, which can reduce the indoor infectious concentration by nearly 99.95% compared with the baseline without any precautionary strategies. With the resumption of school and work in the post-epidemic era, this study would provide intelligence-enhancing advice for the masses and rule-makers to curb the pandemic.

A Review of Spatter in Laser Powder Bed Fusion Additive Manufacturing: In Situ Detection, Generation, Effects, and Countermeasures.

Spatter is an inherent, unpreventable, and undesired phenomenon in laser powder bed fusion (L-PBF) additive manufacturing. Spatter behavior has an intrinsic correlation with the forming quality in L-PBF because it leads to metallurgical defects and the degradation of mechanical properties. This impact becomes more severe in the fabrication of large-sized parts during the multi-laser L-PBF process. Therefore, investigations of spatter generation and countermeasures have become more urgent. Although much research has provided insights into the melt pool, microstructure, and mechanical property, reviews of spatter in L-PBF are still limited. This work reviews the literature on the in situ detection, generation, effects, and countermeasures of spatter in L-PBF. It is expected to pave the way towards a novel generation of highly efficient and intelligent L-PBF systems.

A scalable coaxial bioprinting technology for mesenchymal stem cell microfiber fabrication and high extracellular vesicle yield.

Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising candidates for regenerative medicine; however, the lack of scalable methods for high quantity EV production limits their application. In addition, signature EV-derived proteins shared in 3D environments and 2D surfaces, remain mostly unknown. Herein, we present a platform combining MSC microfiber culture with ultracentrifugation purification for high EV yield. Within this platform, a high quantity MSC solution (∼3 × 108total cells) is encapsulated in a meter-long hollow hydrogel-microfiber via coaxial bioprinting technology. In this 3D core-shell microfiber environment, MSCs express higher levels of stemness markers (Oct4, Nanog, Sox2) than in 2D culture, and maintain their differentiation capacity. Moreover, this platform enriches particles by ∼1009-fold compared to conventional 2D culture, while preserving their pro-angiogenic properties. Liquid chromatography-mass spectrometry characterization results demonstrate that EVs derived from our platform and conventional 2D culturing have unique protein profiles with 3D-EVs having a greater variety of proteins (1023 vs 605), however, they also share certain proteins (536) and signature MSC-EV proteins (10). This platform, therefore, provides a new tool for EV production using microfibers in one culture dish, thereby reducing space, labor, time, and cost.

A facile, versatile hydrogel bioink for 3D bioprinting benefits long-term subaqueous fidelity, cell viability and proliferation.

Both of the long-term fidelity and cell viability of three-dimensional (3D)-bioprinted constructs are essential to precise soft tissue repair. However, the shrinking/swelling behavior of hydrogels brings about inadequate long-term fidelity of constructs, and bioinks containing excessive polymer are detrimental to cell viability. Here, we obtained a facile hydrogel by introducing 1% aldehyde hyaluronic acid (AHA) and 0.375% N-carboxymethyl chitosan (CMC), two polysaccharides with strong water absorption and water retention capacity, into classic gelatin (GEL, 5%)-alginate (ALG, 1%) ink. This GEL-ALG/CMC/AHA bioink possesses weak temperature dependence due to the Schiff base linkage of CMC/AHA and electrostatic interaction of CMC/ALG. We fabricated integrated constructs through traditional printing at room temperature and in vivo simulation printing at 37°C. The printed cell-laden constructs can maintain subaqueous fidelity for 30 days after being reinforced by 3% calcium chloride for only 20 s. Flow cytometry results showed that the cell viability was 91.38 ± 1.55% on day 29, and the cells in the proliferation plateau at this time still maintained their dynamic renewal with a DNA replication rate of 6.06 ± 1.24%. This work provides a convenient and practical bioink option for 3D bioprinting in precise soft tissue repair.

A structure-supporting, self-healing, and high permeating hydrogel bioink for establishment of diverse homogeneous tissue-like constructs.

The ready-to-use, structure-supporting hydrogel bioink can shorten the time for ink preparation, ensure cell dispersion, and maintain the preset shape/microstructure without additional assistance during printing. Meanwhile, ink with high permeability might facilitate uniform cell growth in biological constructs, which is beneficial to homogeneous tissue repair. Unfortunately, current bioinks are hard to meet these requirements simultaneously in a simple way. Here, based on the fast dynamic crosslinking of aldehyde hyaluronic acid (AHA)/N-carboxymethyl chitosan (CMC) and the slow stable crosslinking of gelatin (GEL)/4-arm poly(ethylene glycol) succinimidyl glutarate (PEG-SG), we present a time-sharing structure-supporting (TSHSP) hydrogel bioink with high permeability, containing 1% AHA, 0.75% CMC, 1% GEL and 0.5% PEG-SG. The TSHSP hydrogel can facilitate printing with proper viscoelastic property and self-healing behavior. By crosslinking with 4% PEG-SG for only 3 min, the integrity of the cell-laden construct can last for 21 days due to the stable internal and external GEL/PEG-SG networks, and cells manifested long-term viability and spreading morphology. Nerve-like, muscle-like, and cartilage-like in vitro constructs exhibited homogeneous cell growth and remarkable biological specificities. This work provides not only a convenient and practical bioink for tissue engineering, targeted cell therapy, but also a new direction for hydrogel bioink development.

A 3D engineered scaffold for hematopoietic progenitor/stem cell co-culture in vitro.

Proliferation of HPSCs in vitro can promote its broad clinical therapeutic use. For in vitro co-culture, interaction between the stem cell and feeder cell as well as their spatial position are essential. To imitate the natural microenvironment, a 3D engineered scaffold for CD34+ cells co-culture was established via 3D bioprinting. Herein, the concentration of hydrogel and the ratio of two kinds of cells were optimized. Flow cytometry, real time PCR and RNA-seq technology were applied to analyze the effect of the engineered scaffold on expanded cells. After 10 days co-culture with the engineered scaffold, the expansion of CD34+CD38- cells can reach 33.57-folds and the expansion of CD34+CD184+ cells can reach 16.66-folds. Result of PCR and RNA-seq indicates that the CD34+ cells in 3D group exhibited a tendency of interaction with the engineered scaffold. Compared to 2D co-culture, this customizable 3D engineered scaffold can provide an original and integrated environment for HPSCs growth. Additionally, this scaffold can be modified for different cell co-culture or cell behavior study.