Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy.

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

Topological spin-orbit-coupled fermions beyond rotating wave approximation.

The realization of spin-orbit-coupled ultracold gases has driven a wide range of research and is typically based on the rotating wave approximation (RWA). By neglecting the counter-rotating terms, RWA characterizes a single near-resonant spin-orbit (SO) coupling in a two-level system. Here, we propose and experimentally realize a new scheme for achieving a pair of two-dimensional (2D) SO couplings for ultracold fermions beyond RWA. This work not only realizes the first anomalous Floquet topological Fermi gas beyond RWA, but also significantly improves the lifetime of the 2D-SO-coupled Fermi gas. Based on pump-probe quench measurements, we observe a deterministic phase relation between two sets of SO couplings, which is characteristic of our beyond-RWA scheme and enables the two SO couplings to be simultaneously tuned to the optimum 2D configurations. We observe intriguing band topology by measuring two-ring band-inversion surfaces, quantitatively consistent with a Floquet topological Fermi gas in the regime of high Chern numbers. Our study can open an avenue to explore exotic SO physics and anomalous topological states based on long-lived SO-coupled ultracold fermions.

Catalytic Domino Three-Component Synthesis of Functionalized Heterocycles from Carbon Dioxide.

A catalytic domino, three-component reaction has been developed for the transformation of carbon dioxide into functionalized six-membered cyclic carbonates. The catalytic process combines an initial carboxylative cyclization of ß-epoxy alcohols followed by an oxa-Michael reaction affording an unparalleled scope of heterocyclic structures. The wide range of functional groups, including free-alcohols, empowers the access to a range of products including C11-oxo-based bicyclic heterocycles. The versatility of these functionalized carbonates is further complemented by a series of synthetic diversifications. Control experiments are consistent with the first step of this domino process being promoted by a binary Lewis acid/base catalyst, while the second stage only requires catalytic base.

Preoperative simulated surgery on 3D model assists osteotomy feasibility verification and surgical guidance for patients with cubitus valgus/varus deformity: a retrospective observational study.

BACKGROUND: As the common delayed complication of supracondylar fractures in children, cubitus valgus/varus deformity might lead to pain and loss of motion of the elbow. The current corrective treatment might not be accurate enough and even contribute to postoperative deformity. This study retrospectively analyzed the clinical value of preoperative simulated surgery on 3D model-assisted osteotomy feasibility verification and surgical guidance for cubitus valgus/varus deformity. METHODS: Seventeen patients were selected from October 2016 to November 2019. Deformities were analyzed from imaging data and 3D models and corrected after the simulated operations. The radiographic evaluation comprised osseous union, carrying angle, and anteversion angle of the distal humerus. The clinical evaluation was performed according to the Hospital for Special Surgery (HSS) scoring system. RESULTS: All patients underwent the operation successfully and had no postoperative deformity. The carrying angle was significantly improved postoperatively (P < 0.001). The anteversion angle of the distal humerus did not change significantly (P > 0.05). The HSS score rose after surgery (P < 0.001). The function of the elbow joint was excellent in seven cases and good in ten cases. CONCLUSION: Simulated surgery on 3D model plays an important role in osteotomy plan and surgical guidance, contributing to good surgical efficacy.

Study on Modelling Method of Resilient Mat Used under Floating Slab Track.

Kelvin's model is widely used to simulate the dynamic characteristic of a resilient mat under a slab track. To develop an effective calculation model for a resilient mat using a solid element, a three-parameter viscoelasticity model (3PVM) was employed. With the help of the user-defined material mechanical behavior, the proposed model was implemented in software ABAQUS. To validate the model, a laboratory test was performed on a slab track with a resilient mat. Then, a finite element model of the track-tunnel-soil system was built. The calculation results using the 3PVM was compared with those using Kelvin's model and the test results. The results indicate that the 3PVM can better reflect the dynamic characteristics of resilient mat than Kelvin's model, especially over 10 Hz. Compared with the test results, the 3PVM has an average error of 2.7 dB and a max error of 7.9 dB at 5 Hz.

Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restoration, but still have substantial potential for artifacts. In this study, we developed rationalized deep learning (rDL) for structured illumination microscopy and lattice light sheet microscopy (LLSM) by incorporating prior knowledge of illumination patterns and, thereby, rationally guiding the network to denoise raw images. Here we demonstrate that rDL structured illumination microscopy eliminates spectral bias-induced resolution degradation and reduces model uncertainty by five-fold, improving the super-resolution information by more than ten-fold over other computational approaches. Moreover, rDL applied to LLSM enables self-supervised training by using the spatial or temporal continuity of noisy data itself, yielding results similar to those of supervised methods. We demonstrate the utility of rDL by imaging the rapid kinetics of motile cilia, nucleolar protein condensation during light-sensitive mitosis and long-term interactions between membranous and membrane-less organelles.

The effect of pituitrin on postoperative outcomes in patients with pulmonary hypertension undergoing cardiac surgery: a study protocol for a randomized controlled trial.

Background: The vasoplegic syndrome is one of the major consequences of cardiac surgery. If pulmonary hypertension is additionally involved with vasoplegic syndrome, circulation management becomes much more complicated. According to previous studies, pituitrin (a substitute for vasopressin, which contains vasopressin and oxytocin) not only constricts systemic circulation vessels and increases systemic circulation pressure but also likely decreases pulmonary artery pressure and pulmonary vascular resistance. The aim of this study is to investigate whether pituitrin is beneficial for the postoperative outcomes in patients with pulmonary hypertension undergoing cardiac surgery. Methods and analysis: The randomized controlled trial will include an intervention group continuously infused with 0.04 U/(kg h) of pituitrin and a control group. Adult patients with pulmonary hypertension undergoing elective cardiac surgery will be included in this study. Patients who meet the conditions and give their consent will be randomly assigned to the intervention group or the control group. The primary outcome is the composite endpoint of all-cause mortality within 30 days after surgery or common complications after cardiac surgery. Secondary outcomes include the incidence of other postoperative complications, length of hospital stay, and so on. Discussion: Pituitrin constricts systemic circulation vessels, increases systemic circulation pressure, and may reduce pulmonary artery pressure and pulmonary vascular resistance, which makes it a potentially promising vasopressor during the perioperative period in patients with pulmonary hypertension. Therefore, evidence from randomized controlled trials is necessary to elucidate whether pituitrin influences outcomes in patients with pulmonary hypertension following cardiac surgery.

Research on multi-class orthodontic image recognition system based on deep learning network model / 中华口腔医学杂志

Objective: To develop a multi-classification orthodontic image recognition system using the SqueezeNet deep learning model for automatic classification of orthodontic image data. Methods: A total of 35 000 clinical orthodontic images were collected in the Department of Orthodontics, Capital Medical University School of Stomatology, from October to November 2020 and June to July 2021. The images were from 490 orthodontic patients with a male-to-female ratio of 49∶51 and the age range of 4 to 45 years. After data cleaning based on inclusion and exclusion criteria, the final image dataset included 17 453 face images (frontal, smiling, 90° right, 90° left, 45° right, and 45° left), 8 026 intraoral images [frontal occlusion, right occlusion, left occlusion, upper occlusal view (original and flipped), lower occlusal view (original and flipped) and coverage of occlusal relationship], 4 115 X-ray images [lateral skull X-ray from the left side, lateral skull X-ray from the right side, frontal skull X-ray, cone-beam CT (CBCT), and wrist bone X-ray] and 684 other non-orthodontic images. A labeling team composed of orthodontic doctoral students, associate professors, and professors used image labeling tools to classify the orthodontic images into 20 categories, including 6 face image categories, 8 intraoral image categories, 5 X-ray image categories, and other images. The data for each label were randomly divided into training, validation, and testing sets in an 8∶1∶1 ratio using the random function in the Python programming language. The improved SqueezeNet deep learning model was used for training, and 13 000 natural images from the ImageNet open-source dataset were used as additional non-orthodontic images for algorithm optimization of anomaly data processing. A multi-classification orthodontic image recognition system based on deep learning models was constructed. The accuracy of the orthodontic image classification was evaluated using precision, recall, F1 score, and confusion matrix based on the prediction results of the test set. The reliability of the model's image classification judgment logic was verified using the gradient-weighted class activation mapping (Grad-CAM) method to generate heat maps. Results: After data cleaning and labeling, a total of 30 278 orthodontic images were included in the dataset. The test set classification results showed that the precision, recall, and F1 scores of most classification labels were 100%, with only 5 misclassified images out of 3 047, resulting in a system accuracy of 99.84%(3 042/3 047). The precision of anomaly data processing was 100% (10 500/10 500). The heat map showed that the judgment basis of the SqueezeNet deep learning model in the image classification process was basically consistent with that of humans. Conclusions: This study developed a multi-classification orthodontic image recognition system for automatic classification of 20 types of orthodontic images based on the improved SqueezeNet deep learning model. The system exhibitted good accuracy in orthodontic image classification.

Automated diagnostic classification with lateral cephalograms based on deep learning network model / 中华口腔医学杂志

Objective: To establish a comprehensive diagnostic classification model of lateral cephalograms based on artificial intelligence (AI) to provide reference for orthodontic diagnosis. Methods: A total of 2 894 lateral cephalograms were collected in Department of Orthodontics, Capital Medical University School of Stomatology from January 2015 to December 2021 to construct a data set, including 1 351 males and 1 543 females with a mean age of (26.4± 7.4) years. Firstly, 2 orthodontists (with 5 and 8 years of orthodontic experience, respectively) performed manual annotation and calculated measurement for primary classification, and then 2 senior orthodontists (with more than 20 years of orthodontic experience) verified the 8 diagnostic classifications including skeletal and dental indices. The data were randomly divided into training, validation, and test sets in the ratio of 7∶2∶1. The open source DenseNet121 was used to construct the model. The performance of the model was evaluated by classification accuracy, precision rate, sensitivity, specificity and area under the curve (AUC). Visualization of model regions of interest through class activation heatmaps. Results: The automatic classification model of lateral cephalograms was successfully established. It took 0.012 s on average to make 8 diagnoses on a lateral cephalogram. The accuracy of 5 classifications was 80%-90%, including sagittal and vertical skeletal facial pattern, mandibular growth, inclination of upper incisors, and protrusion of lower incisors. The acuracy rate of 3 classifications was 70%-80%, including maxillary growth, inclination of lower incisors and protrusion of upper incisors. The average AUC of each classification was ≥0.90. The class activation heat map of successfully classified lateral cephalograms showed that the AI model activation regions were distributed in the relevant structural regions. Conclusions: In this study, an automatic classification model for lateral cephalograms was established based on the DenseNet121 to achieve rapid classification of eight commonly used clinical diagnostic items.

Novel glycosylation zinc(II)-cryptolepine complexes perturb mitophagy pathways and trigger cancer cell apoptosis and autophagy in SK-OV-3/DDP cells.

With the aim of shedding some light on the mechanism of action of zinc(II) complexes in antiproliferative processes and molecular signaling pathways, three novel glycosylated zinc(II)-cryptolepine complexes, i.e., [Zn(QA1)Cl2] (Zn(QA1)), [Zn(QA2)Cl2] (Zn(QA2)), and [Zn(QA3)Cl2] (Zn(QA3)), were prepared by conjugating a glucose moiety with cryptolepine, followed by complexation of the resulting glycosylated cryptolepine compounds N-((1-(2-morpholinoethyl)-1H-1,2,3-triazol-4-yl)methyl)-benzofuro[3,2-b]quinolin-11-amine (QA1), 2-(4-((benzofuro[3,2-b]quinolin-11-ylamino)methyl)-1H-1,2,3-triazol-1-yl)ethan-1-ol (QA2), and (2S,3S,4R,5R,6S)-2-(4-((benzofuro[3,2-b]quinolin-11-ylamino)-methyl)-1H-1,2,3-triazol-1-yl)-6-(hydroxymethyl)tetrahydro-2H-pyran-3,4,5-triol (QA3) with zinc(II), and their anticancer activity was evaluated. In MTT assays, Zn(QA1)-Zn(QA3) were more active against cisplatin-resistant ovarian SK-OV-3/DDP cancer cells (SK-OV-3cis) than ZnCl2 and the QA1-QA3 ligands, with IC50 values of 1.81 ± 0.50, 2.92 ± 0.32, and 1.01 ± 0.11 µM, respectively. Complexation of glycosylated cryptolepine QA3 with zinc(II) increased the antiproliferative activity of the ligand, suggesting that Zn(QA3) could act as a chaperone to deliver the active ligand intracellularly, in contrast with other cryptolepine metal complexes previously reported. In vivo and in vitro investigations suggested that Zn(QA3) exhibited enhanced anticancer activity with treatment effects comparable to those of the clinical drug cisplatin. Furthermore, Zn(QA1)-Zn(QA3) triggered SK-OV-3cis cell apoptosis through mitophagy pathways in the order Zn(QA1) > Zn(QA1) > Zn(QA2). These results demonstrate the potential of glycosylated zinc(II)-cryptolepine complexes for the development of chemotherapy drugs against cisplatin-resistant SK-OV-3cis cells.

Novel bifluorescent Zn(II)-cryptolepine-cyclen complexes trigger apoptosis induced by nuclear and mitochondrial DNA damage in cisplatin-resistant lung tumor cells.

Four novel bifluorescent Zn(II)-cryptolepine-cyclen complexes, namely [Zn(BQTC)]Cl2 (Zn(BQTC)), [Zn(BQA) (Cur)Cl] (Zn(BQACur)), [Zn (TC)]Cl2 (Zn(TC)), and [Zn (AP) (Cur)Cl] (Zn(APCur)), bearing curcumin (H-Cur), cyclen (TC), 1,10-phenanthrolin-5-amine (AP), and novel cryptolepine-cyclen derivatives (BQTC and BQA) were prepared for cell nucleus- and mitochondria-specific imaging. MTT assay results indicated that Zn(BQTC) and Zn(BQACur) exhibit stronger anticancer activity against cisplatin-resistant A549R lung tumor cells than ZnCl2, Zn(TC), Zn(APCur), H-Cur, TC, AP, BQTC, and BQA. Due to the dual fluorescence characteristic of Zn(BQTC), selective fluorescence imaging of the nucleus and mitochondria of A549R cancer cells was conducted. Further, Zn(BQTC), obtained by the functionalization of Zn(TC) with cryptolepine derivative substituents, efficiently inhibited DNA synthesis, thus resulting in high cytotoxicity (selective for A549R lung tumor cells) accompanied by DNA impairment in nuclear and mitochondrial fractions. Additionally, Zn(BQTC) caused severe damage to the mitochondrial DNA (mtDNA) and nuclear DNA (nDNA), sequentially disrupted mitochondrial and nuclear functions, and promoted the DNA damage-induced apoptotic signaling pathway and adenosine triphosphate depletion (ATP). Thus, Zn(BQTC) can be used as an anticancer drug by targeting mtDNA and nDNA. Most importantly, Zn(BQTC) showed higher efficacy in inhibiting cancer growth (55.9%) in A549R tumor-bearing mice than Zn(TC) (31.2%) and cisplatin, along with a promising in vivo safety profile. These results demonstrate the applicability of the developed novel bifluorescent Zn(II)-cryptolepine-cyclen complexes as promising DNA-targeting anticancer agents for cancer treatment.

A new class of nickel(II) oxyquinoline-bipyridine complexes as potent anticancer agents induces apoptosis and autophagy in A549/DDP tumor cells through mitophagy pathways.

A new class of nickel(II) oxyquinoline-bipyridine complexes, namely, [Ni(La1)2(Lb6)] (Ni1), [Ni(La1)2(Lb2)] ·CH3OH (Ni2), [Ni(La7)2(Lb11)]·2H2O (Ni3), [Ni(La1)2(Lb9)] (Ni4), [Ni(La1)2(Lb8)] (Ni5), [Ni(La2)2(Lb1)] (Ni6), [Ni(La2)2(Lb6)]·CH3OH (Ni7), [Ni(La2)2(Lb11)]·CH3OH (Ni8), [Ni(La2)2(Lb3)] (Ni9), [Ni(La2)2(Lb2)]·CH3OH (Ni10), [Ni(La2)2(Lb5)]·CH3OH (Ni11), [Ni(La2)2(Lb7)] (Ni12), [Ni(La3)2(Lb2)] (Ni13), [Ni(La4)2(Lb4)]·2CH3OH (Ni14), [Ni(La4)2(Lb8)]·2.5CH3OH (Ni15), [Ni(La4)2(Lb11)]·1.5CH3OH (Ni16), [Ni(La5)2(Lb7)] (Ni17), [Ni(La5)2(Lb10)]·CH3OH (Ni18), [Ni(La6)2(Lb11)]·3CH3OH (Ni19), [Ni(La7)2(Lb7)]·2CH3OH (Ni20), [Ni(La7)2(Lb8)]·2CH3OH (Ni21) and [Ni(La7)2(Lb1)]·2CH3OH (Ni22) bearing oxyquinoline (H-La1-H-La7) and bipyridine derivatives (Lb1-Lb11) were synthesized and characterized by elemental analysis, X-ray crystallography, infrared (IR) spectroscopy and electrospray mass spectrometry (ESI-MS). An MTT method suggested that the IC50 values of Ni1-Ni22 for A549/DDP tumor cells were 0.25-25.14 µM, but these complexes exhibited low cytotoxicity toward normal HL-7702 cells (>50 µM). Ni2 could induce A549/DDP tumor cell apoptosis, cause a decrease in the mitochondrial membrane potential (MMP, ΔΨm), and increase the intracellular [Ca2+] and reactive oxygen species (ROS) levels better than Ni10, Ni13, and Ni14. Autophagic and western blot assays showed that Ni2, Ni10, Ni13, and Ni14 could induce autophagy and regulate the expression of LC3 II/I, Beclin1, P62, PINK1, and Parkin proteins, and the inducibility activities were in the order of Ni2 > Ni14 > Ni13 > Ni10. Taken together, these results revealed that the nickel(II) oxyquinoline-bipyridine complex Ni2 inhibited cell growth in A549/DDP tumor cells via mitophagy pathways.

A Novel Catalytic Route to Polymerizable Bicyclic Cyclic Carbonate Monomers from Carbon Dioxide.

A new catalytic route has been developed for the coupling of epoxides and CO2 affording polymerizable six-membered bicyclic carbonates. Cyclic epoxides equipped with a ß-positioned OH group can be transformed into structurally diverse bicyclic cyclic carbonates in good yields and with high selectivity. Key to the chemo-selectivity is the difference between the reactivity of syn- and anti-configured epoxy alcohols, with the latter leading to six-membered ring carbonate formation in the presence of a binary AlIII aminotriphenolate complex/DIPEA catalyst. X-ray analyses show that the conversion of the syn-configured substrate evolves via a standard double inversion pathway providing a five-membered carbonate product, whereas the anti-isomer allows for activation of the oxirane unit of the substrate opposite to the pendent alcohol. The potential use of these bicyclic products is shown in ring-opening polymerization offering access to rigid polycarbonates with improved thermal resistance.

Duodenases are a small subfamily of ruminant intestinal serine proteases that have undergone a remarkable diversification in cleavage specificity.

Ruminants have a very complex digestive system adapted for the digestion of cellulose rich food. Gene duplications have been central in the process of adapting their digestive system for this complex food source. One of the new loci involved in food digestion is the lysozyme c locus where cows have ten active such genes compared to a single gene in humans and where four of the bovine copies are expressed in the abomasum, the real stomach. The second locus that has become part of the ruminant digestive system is the chymase locus. The chymase locus encodes several of the major hematopoietic granule proteases. In ruminants, genes within the chymase locus have duplicated and some of them are expressed in the duodenum and are therefore called duodenases. To obtain information on their specificities and functions we produced six recombinant proteolytically active duodenases (three from cows, two from sheep and one from pigs). Two of the sheep duodenases were found to be highly specific tryptases and one of the bovine duodenases was a highly specific asp-ase. The remaining two bovine duodenases were dual enzymes with potent tryptase and chymase activities. In contrast, the pig enzyme was a chymase with no tryptase or asp-ase activity. These results point to a remarkable flexibility in both the primary and extended specificities within a single chromosomal locus that most likely has originated from one or a few genes by several rounds of local gene duplications. Interestingly, using the consensus cleavage site for the bovine asp-ase to screen the entire bovine proteome, it revealed Mucin-5B as one of the potential targets. Using the same strategy for one of the sheep tryptases, this enzyme was found to have potential cleavage sites in two chemokine receptors, CCR3 and 7, suggesting a role for this enzyme to suppress intestinal inflammation.

Evaluation and development of deep neural networks for image super-resolution in optical microscopy.

Deep neural networks have enabled astonishing transformations from low-resolution (LR) to super-resolved images. However, whether, and under what imaging conditions, such deep-learning models outperform super-resolution (SR) microscopy is poorly explored. Here, using multimodality structured illumination microscopy (SIM), we first provide an extensive dataset of LR-SR image pairs and evaluate the deep-learning SR models in terms of structural complexity, signal-to-noise ratio and upscaling factor. Second, we devise the deep Fourier channel attention network (DFCAN), which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information about diverse biological structures. Third, we show that DFCAN's Fourier domain focalization enables robust reconstruction of SIM images under low signal-to-noise ratio conditions. We demonstrate that DFCAN achieves comparable image quality to SIM over a tenfold longer duration in multicolor live-cell imaging experiments, which reveal the detailed structures of mitochondrial cristae and nucleoids and the interaction dynamics of organelles and cytoskeleton.

Instant multicolor super-resolution microscopy with deep convolutional neural network.

Multicolor super-resolution (SR) microscopy plays a critical role in cell biology research and can visualize the interactions between different organelles and the cytoskeleton within a single cell. However, more color channels bring about a heavier budget for imaging and sample preparation, and the use of fluorescent dyes of higher emission wavelengths leads to a worse spatial resolution. Recently, deep convolutional neural networks (CNNs) have shown a compelling capability in cell segmentation, super-resolution reconstruction, image restoration, and many other aspects. Taking advantage of CNN's strong representational ability, we devised a deep CNN-based instant multicolor super-resolution imaging method termed IMC-SR and demonstrated that it could be used to separate different biological components labeled with the same fluorophore, and generate multicolor images from a single super-resolution image in silico. By IMC-SR, we achieved fast three-color live-cell super-resolution imaging with ~100 nm resolution over a long temporal duration, revealing the complicated interactions between multiple organelles and the cytoskeleton in a single COS-7 cell.

Enhanced reconstruction of structured illumination microscopy on a polarized specimen.

Structured illumination microscopy (SIM) requires polarization control to guarantee the high-contrast illumination pattern. However, this modulated polarization will induce artifacts in SIM when imaging fluorescent dipoles. Here we proposed the polarization weighted recombination of frequency components to reconstruct SIM data with suppressed artifacts and better resolving power. Both the simulation results and experimental data demonstrate that our algorithm can obtain isotropic resolution on dipoles and resolve a clearer structure in high-density sections compared to the conventional algorithm. Our work reinforces the SIM theory and paves the avenue for the application of SIM on a polarized specimen.

Organocatalytic Trapping of Elusive Carbon Dioxide Based Heterocycles by a Kinetically Controlled Cascade Process.

A conceptually novel approach is described for the synthesis of six-membered cyclic carbonates derived from carbon dioxide. The approach utilizes homoallylic precursors that are converted into five-membered cyclic carbonates having a ß-positioned alcohol group in one of the ring substituents. The activation of the pendent alcohol group through an N-heterocyclic base allows equilibration towards a thermodynamically disfavored six-membered carbonate analogue that can be trapped by an acylating agent. Various control experiments and computational analysis of this manifold are in line with a process that is primarily dictated by a kinetically controlled acylation step. This cascade process delivers an ample diversity of six-membered cyclic carbonates in excellent yields and chemoselectivities under mild reaction conditions.

Maxillary protraction using customized mini-plates for anchorage in an adolescent girl with skeletal Class III malocclusion / 대한치과교정학회지

The treatment of skeletal Class III malocclusion in adolescents is challenging.Maxillary protraction, particularly that using bone anchorage, has been proven to be an effective method for the stimulation of maxillary growth. However, the conventional procedure, which involves the surgical implantation of mini-plates, is traumatic and associated with a high risk. Three-dimensional (3D) digital technology offers the possibility of individualized treatment. Customized miniplates can be designed according to the shape of the maxillary surface and the positions of the roots on cone-beam computed tomography scans; this reduces both the surgical risk and patient trauma. Here we report a case involving a 12-year-old adolescent girl with skeletal Class III malocclusion and midface deficiency that was treated in two phases. In phase 1, rapid maxillary expansion and protraction were performed using 3D-printed mini-plates for anchorage.The mini-plates exhibited better adaptation to the bone contour, and titanium screw implantation was safer because of the customized design. The orthopedic force applied to each mini-plate was approximately 400–500 g, and the plates remained stable during the maxillary protraction process, which exhibited efficacious orthopedic effects and significantly improved the facial profile and esthetics. In phase 2, fixed appliances were used for alignment and leveling of the maxillary and mandibular dentitions. The complete two-phase treatment lasted for 24 months. After 48 months of retention, the treatment outcomes remained stable.