An X-ray analysis of exfoliated MoS2, produced by means of microwave-assisted liquid-phase exfoliation (LPE) from bulk powder in 1-methyl-2-pyrrolidone (NMP) or acetonitrile (ACN) + 1-methyl-2-pyrrolidone (NMP) solvents, has revealed distinct structural differences between the bulk powder and the microwave-exfoliated samples. Specifically, we performed X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) measurements to identify the elements of our exfoliated sample deposited on a Si substrate by drop-casting, as well as their chemical state and its structural crystalline phase. In the exfoliated sample, the peaks pattern only partially resemble the theoretical Miller indices for MoS2. In contrast, the bulk powder\'s spectrum shows the characteristic peaks of the 2H polytype of MoS2, but with some broadening. Notable is the retention of partial crystallinity in the post-exfoliation phases, specifically in the normal-to-plane orientation, thus demonstrating the effectiveness of microwave-assisted techniques in producing 2D MoS2 and attaining desirable properties for the material. XPS measurements confirm the success of the exfoliation procedure and that the exfoliated sample retains its original structure. The exfoliation process has been optimized to maintain the structural integrity of MoS2 while enhancing its surface area and electrochemical performance, thereby making it a promising material for advanced electronic and optoelectronic applications ranging from energy storage to sensing devices under ambient conditions.

Heterostructure engineering is crucial for enhancing gas sensing performance. However, achieving rapid response for room-temperature NO2 sensing through rational heterostructure design remains a challenge. In this study, a Bi2Se3/SnSe2 2D/2D heterostructure was synthesized by hydrothermal method for the rapid detection of NO2 at room temperature. By combining Bi2Se3 nanosheets with SnSe2 nanosheets, the Bi2Se3/SnSe2 sensor demonstrated and the lowest detection limit for NO2 a short response time (15 s) to 10 ppm NO2 at room temperature, reaches 25 ppb. Furthermore the sensor demonstrates significantly larger response to NO2 than to other interfering gases, including 10 ppm NO2, H2S, NH3, CH4, CO, and SO2,demonstrating its outstanding selectivity. And we discuss the mechanism of related performance enhancement.

BACKGROUND: In veterinary medicine, previous studies regarding the diagnostic performance of shear wave elastography (SWE) in chronic kidney disease (CKD) are not consistent with each other. Moreover, there has been no study evaluating the relationship between symmetric dimethyl arginine (SDMA) concentration and renal shear wave velocity (SWV) using two-dimensional SWE (2D SWE) in dogs with CKD.
OBJECTIVE: This study aimed to evaluate the diagnostic capability of 2D SWE in dogs with CKD and to assess the relationship between renal SWV and SDMA concentration.
METHODS: Dogs with healthy kidneys and dogs with CKD underwent 2D SWE and SDMA assay. Renal stiffness was estimated as renal SWV in m/s.
RESULTS: SDMA concentration had a weak positive correlation with the left (r = 0.338, p = 0.022) and right renal SWV (r = 0.337, p = 0.044). Renal SWV was not significantly different between healthy kidney and CKD groups in the left (p = 0.085) and right (p = 0.171) kidneys.
CONCLUSIONS: 2D SWE may could not distinguish between dogs with healthy kidney and dogs with early stage of CKD, but it would be useful for assessing the serial change of renal function in dogs.

Multiplex immunofluorescence (MxIF) is an emerging imaging technology whose downstream molecular analytics highly rely upon the effectiveness of cell segmentation. In practice, multiple membrane markers (e.g., NaKATPase, PanCK and β-catenin) are employed to stain membranes for different cell types, so as to achieve a more comprehensive cell segmentation since no single marker fits all cell types. However, prevalent watershed-based image processing might yield inferior capability for modeling complicated relationships between markers. For example, some markers can be misleading due to questionable stain quality. In this paper, we propose a deep learning based membrane segmentation method to aggregate complementary information that is uniquely provided by large scale MxIF markers. We aim to segment tubular membrane structure in MxIF data using global (membrane markers z-stack projection image) and local (separate individual markers) information to maximize topology preservation with deep learning. Specifically, we investigate the feasibility of four SOTA 2D deep networks and four volumetric-based loss functions. We conducted a comprehensive ablation study to assess the sensitivity of the proposed method with various combinations of input channels. Beyond using adjusted rand index (ARI) as the evaluation metric, which was inspired by the clDice, we propose a novel volumetric metric that is specific for skeletal structure, denoted as clDiceSKEL. In total, 80 membrane MxIF images were manually traced for 5-fold cross-validation. Our model outperforms the baseline with a 20.2% and 41.3% increase in clDiceSKEL and ARI performance, which is significant (p<0.05) using the Wilcoxon signed rank test. Our work explores a promising direction for advancing MxIF imaging cell segmentation with deep learning membrane segmentation. Tools are available at https://github.com/MASILab/MxIF_Membrane_Segmentation.

Background: Revision hip arthroplasty presents a surgical challenge, necessitating meticulous preoperative planning to avert complications like periprosthetic fractures and aseptic loosening. Historically, assessment of the accuracy of three-dimensional (3D) versus two-dimensional (2D) templating has focused exclusively on primary hip arthroplasty. Materials and Methods: In this retrospective study, we examined the accuracy of 3D templating for acetabular revision cups in 30 patients who underwent revision hip arthroplasty. Utilizing computed tomography scans of the patients\' pelvis and 3D templates of the implants (Aesculap Plasmafit, B. Braun; Aesculap Plasmafit Revision, B. Braun; Avantage Acetabular System, Zimmerbiomet, EcoFit 2M, Implantcast; Tritanium Revision, Stryker), we performed 3D templating and positioned the acetabular cup implants accordingly. To evaluate accuracy, we compared the planned sizes of the acetabular cups in 2D and 3D with the sizes implanted during surgery. Results: An analysis was performed to examine potential influences on templating accuracy, specifically considering factors such as gender and body mass index (BMI). Significant statistical differences (p < 0.001) in the accuracy of size prediction were observed between 3D and 2D templating. Personalized 3D templating exhibited an accuracy rate of 66.7% for the correct prediction of the size of the acetabular cup, while 2D templating achieved an exact size prediction in only 26.7% of cases. There were no statistically significant differences between the 2D and 3D templating methods regarding gender or BMI. Conclusion: This study demonstrates that 3D templating improves the accuracy of predicting acetabular cup sizes in revision arthroplasty when compared to 2D templating. However, it should be noted that the predicted implant size generated through 3D templating tended to overestimate the implanted implant size by an average of 1.3 sizes.

The Special Issue covers low-dimensional structures or systems with reduced spatial dimensions, resulting in unique properties. The classification of these materials according to their dimensionality (0D, 1D, 2D, etc.) emerged from nanoscience and nanotechnology. One review and eighteen research articles highlight recent developments and perspectives in the field of low-dimensional structures and demonstrate the potential of low-dimensional systems in various fields, from nanomaterials for energy applications to biomedical sensors and biotechnology sector.

Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012-2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial-mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.

(1) Background: Facial scanners are used in different fields of dentistry to digitalize the soft tissues of the patient\'s face. The development of technology has allowed the patient to have a 3-dimensional virtual representation, facilitating facial integration in the diagnosis and treatment plan. However, the accuracy of the facial scanner and the obtaining of better results with respect to the manual or two-dimensional (2D) method are questionable. The objective of this clinical trial was to evaluate the usefulness and accuracy of the 3D method (a dual-structured light facial scanner) and compare it with the 2D method (photography) to obtain facial analysis in the maximum intercuspation position and smile position. (2) Methods: A total of 60 participants were included, and nine facial landmarks and five interlandmarks distances were determined by two independent calibrated operators for each participant. All measurements were made using three methods: the manual method (manual measurement), the 2D method (photography), and the 3D method (facial scanner). All clinical and lighting conditions, as well as the specific parameters of each method, were standardized and controlled. The facial interlandmark distances were made by using a digital caliper, a 2D software program (Adobe Photoshop, version 21.0.2), and a 3D software program (Meshlab, version 2020.12), respectively. The data were analyzed by SPSS statistical software. The Kolmogorov-Smirnov test revealed that trueness and precision values were normally distributed (p > 0.05), so a Student\'s t-test was employed. (3) Results: Statistically significant differences (p ≤ 0.01) were observed in all interlandmark measurements in the 2D group (photography) to compare with the manual group. The 2D method obtained a mean accuracy value of 2.09 (±3.38) and 2.494 (±3.67) in maximum intercuspation and smile, respectively. On the other hand, the 3D method (facial scanner) obtained a mean accuracy value of 0.61 (±1.65) and 0.28 (±2.03) in maximum intercuspation and smile, respectively. There were no statistically significant differences with the manual method. (4) Conclusions: The employed technique demonstrated that it influences the accuracy of facial records. The 3D method reported acceptable accuracy values, while the 2D method showed discrepancies over the clinically acceptable limits.

The key to designing and fabricating highly efficient mixed protonic-electronic conductors materials (MPECs) is to integrate the mixed conductive active sites into a single structure, to break through the shortcomings of traditional physical blending. Herein, based on the host-guest interaction, an MPEC is consisted of 2D metal-organic layers and hydrogen-bonded inorganic layers by the assembly methods of layered intercalation. Noticeably, the 2D intercalated materials (≈1.3 nm) exhibit the proton conductivity and electron conductivity, which are 2.02 × 10-5 and 3.84 × 10-4 S cm-1 at 100 °C and 99% relative humidity, much higher than these of pure 2D metal-organic layers (>>1.0 × 10-10 and 2.01×10-8 S cm-1 ), respectively. Furthermore, combining accurate structural information and theoretical calculations reveals that the inserted hydrogen-bonded inorganic layers provide the proton source and a networks of hydrogen-bonds leading to efficient proton transport, meanwhile reducing the bandgap of hybrid architecture and increasing the band electron delocalization of the metal-organic layer to greatly elevate the electron transport of intrinsic 2D metal-organic frameworks.

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions-self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.