Effects of a diabetes education program integrated with text-message support for lifestyle change among older individuals with type 2 diabetes in communities: a randomised controlled trial.

OBJECTIVE: The objective of this study was to investigate the effects of self-management education integrated with text-message support (SME-TMS) on glycaemic control in individuals with type 2 diabetes. STUDY DESIGN: a randomized, controlled trial. METHODS: Patients from two communities were randomized into the intervention group (n = 53) or the control group (n = 52). The six-month intervention included the culturally tailored diabetes education and text-messaging support for behaviour changes. The control group received treatment as usual. The primary outcome was reductions in HbA1c and fasting blood glucose at six-month non-intervention follow-up. Secondary outcomes were reductions in body weight, body mass index (BMI), blood pressure, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, physical activity, and health beliefs. RESULTS: The intervention led to substantially increase days of weekly physical activity (42% vs. 0%, P < 0.001) and health beliefs (coefficient = 7.0, 95% confidence interval [CI]: 4.4 to 9.6, P < 0.001). However, no greater reduction was found in HbA1c at six months after the intervention, compared with the control group (0.13%, 95% CI: -0.20 to 0.46, P = 0.443). The reductions of blood pressure, TC, and LDL-C were greater in the control group than in the intervention group (all P < 0.050). Within the intervention group, participants had significant reduction in BMI, whereas the control group had greater reductions in TC and LDL-C (all P < 0.050). CONCLUSIONS: The SME-TMS intervention led to a greater increase in the weekly physical activity and health belief score in the older patients at 6-month follow-up than with the usual care. Further research is needed to ascertain how these benefits could be translated into favorable medium-and long-term glycaemic control. TRAIL REGISTRATION NUMBER: This study was registered on Chinese Clinical Trials Registry (ChiCTR2300075112).

Machine learning-enabled forward prediction and inverse design of 4D-printed active plates.

Shape transformations of active composites (ACs) depend on the spatial distribution of constituent materials. Voxel-level complex material distributions can be encoded by 3D printing, offering enormous freedom for possible shape-change 4D-printed ACs. However, efficiently designing the material distribution to achieve desired 3D shape changes is significantly challenging yet greatly needed. Here, we present an approach that combines machine learning (ML) with both gradient-descent (GD) and evolutionary algorithm (EA) to design AC plates with 3D shape changes. A residual network ML model is developed for the forward shape prediction. A global-subdomain design strategy with ML-GD and ML-EA is then used for the inverse material-distribution design. For a variety of numerically generated target shapes, both ML-GD and ML-EA demonstrate high efficiency. By further combining ML-EA with a normal distance-based loss function, optimized designs are achieved for multiple irregular target shapes. Our approach thus provides a highly efficient tool for the design of 4D-printed active composites.

[Ocular surface adverse reactions of antitumor-targeted drugs].

With the extensive application of targeted drugs, the survival rate of cancer patients has been significantly improved. However, adverse reactions to the drugs have also become apparent, especially those affecting the ocular surface, which can severely impact patients' vision and quality of life. The article systematically analyzes a variety of targeted drugs, including epidermal growth factor receptor inhibitors, human epidermal growth factor receptor 2 inhibitors, fibroblast growth factor receptor inhibitors, selective estrogen receptor modulators, vascular endothelial growth factor receptor inhibitors, aromatase inhibitors, proteasome inhibitors, antibody-drug conjugates, Bruton's tyrosine kinase inhibitors, FMS-like tyrosine kinase 3 inhibitors, and cyclin-dependent kinase inhibitors, and discusses their adverse reactions on the ocular surface. The review emphasizes the role of clinicians in monitoring and managing patients' ocular surface health and the importance of early diagnosis and intervention to ensure that patients receive optimal visual protection while undergoing antitumor treatment.

[Clinicopathological and molecular genetic features of confined placental mosaicism].

Objective: To investigate the clinicopathological and genetic features of confined placental mosaicism (CPM) and its effect on fetal intrauterine growth. Methods: Fourteen CPM cases of Haidian Maternal and Children Health Hospital were collected from May 2018 to March 2022. Clinicopathological examination on placental specimens and molecular genetic analysis were performed. Results: The age of the parturient women ranged from 27 to 34 years, with an average age of (30.0±3.54) years. The gestational weeks ranged from 35+1 to 41+2 weeks. There were 4 premature births and 10 term births, among which 6 were female and 8 were male fetuses. Nine cases (9/14) had adverse pregnancy outcomes, including 7 cases of fetal growth restriction. The weight of CPM placenta decreased, with 6 cases below the 10th percentile of weight standards and 5 cases between the 10th and 25th percentile. All 14 CPM placental specimens showed morphological changes of perfusion dysfunction to varying degrees, with mainly placental-maternal vascular malperfusion followed by placental-fetal vascular malperfusion. The mosaic chromosomes in different CPM cases varied, with 16-trisomy/monosomy mosaicism being the most common followed by 7-trisomy and 21-trisomy/monosomy mosaicism. The mosaic proportion was unequal in different parts of the same CPM placenta, with the mosaic proportion of umbilical cord, fetal membranes, fetal surface, maternal surface, and edge ranging from 1% to 70%. Conclusions: The mosaic chromosomes in different CPM cases vary, and the mosaic proportion is unequal in different parts of the same CPM placenta. The pathological morphology is mainly manifested as perfusion dysfunction, which can lead to adverse pregnancy outcomes such as fetal growth restriction and preterm birth.

Computational synthesis of locomotive soft robots by topology optimization.

Locomotive soft robots (SoRos) have gained prominence due to their adaptability. Traditional locomotive SoRo design is based on limb structures inspired by biological organisms and requires human intervention. Evolutionary robotics, designed using evolutionary algorithms (EAs), have shown potential for automatic design. However, EA-based methods face the challenge of high computational cost when considering multiphysics in locomotion, including materials, actuations, and interactions with environments. Here, we present a design approach for pneumatic SoRos that integrates gradient-based topology optimization with multiphysics material point method (MPM) simulations. This approach starts with a simple initial shape (a cube with a central cavity). The topology optimization with MPM then automatically and iteratively designs the SoRo shape. We design two SoRos, one for walking and one for climbing. These SoRos are 3D printed and exhibit the same locomotion features as in the simulations. This study presents an efficient strategy for designing SoRos, demonstrating that a purely mathematical process can produce limb-like structures seen in biological organisms.

Michael Addition-Elimination Ring-Opening Polymerization.

A cyclic thioenone system capable of controlled ring-opening polymerization (ROP) is presented that leverages a reversible Michael addition-elimination (MAE) mechanism. The cyclic thioenone monomers are easy to access and modify and for the first time incorporate the dynamic reversibility of MAE with chain-growth polymerization. This strategy features mild polymerization conditions, tunable functionalities, controlled molecular weights (Mn), and narrow dispersities. The obtained polythioenones exhibit excellent optical transparency and good mechanical properties and can be depolymerized to recover the original monomers. Density functional theory (DFT) calculations of model reactions offer insights into the role of monomer conformation in the polymerization process, as well as explaining divergent reactivity observed in seven-membered thiepane (TP) and eight-membered thiocane (TC) ring systems. Collectively, these findings demonstrate the feasibility of MAE mechanisms in ring-opening polymerization and provide important guidelines toward future monomer designs.

MRI radiomics predicts the efficacy of EGFR-TKI in EGFR-mutant non-small-cell lung cancer with brain metastasis.

AIM: To develop and validate models based on magnetic resonance imaging (MRI) radiomics for predicting the efficacy of epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) in EGFR-mutant non-small-cell lung cancer (NSCLC) patients with brain metastases. MATERIALS AND METHODS: 117 EGFR-mutant NSCLC patients with brain metastases who received EGFR-TKI treatment were included in this study from January 1, 2014 to December 31, 2021. Patients were randomly divided into training and validation cohorts in a ratio of 2:1. Radiomics features extracted from brain MRI were screened by least absolute shrinkage and selection operator (LASSO) algorithm. Logistic regression analysis and Cox proportional hazard regression analysis were used to screen clinical risk factors. Clinical (C), radiomics (R), and combined (C + R) nomograms were constructed in models predicting short-term efficacy and intracranial progression-free survival (iPFS), respectively. Calibration curves, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of models. RESULTS: Overall response rate (ORR) was 57.3% and median iPFS was 12.67 months. The C + R nomograms were more effective. In the short-term efficacy model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.860 (0.820-0.901, 95%CI) and 0.843 (0.783-0.904, 95%CI). In iPFS model, the C-indexes of C + R nomograms in training cohort and validation cohort were 0.837 (0.751-0.923, 95%CI) and 0.850 (0.763-0.937, 95%CI). CONCLUSION: The C + R nomograms were more effective in predicting EGFR-TKI efficacy of EGFR-mutant NSCLC patients with brain metastases than single clinical or radiomics nomograms.

Results of an interlaboratory study on the working curve in vat photopolymerization.

The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs radiant exposure of light is now a foundational measurement in the field of PAM. Despite its widespread use in industry and academia, there is no formal method or procedure for performing the working curve measurement, raising questions about the utility of reported working curve parameters. Here, an interlaboratory study (ILS) is described in which 24 individual laboratories performed a working curve measurement on an aliquot from a single batch of PAM resin. The ILS reveals that there is enormous scatter in the working curve data and the key fit parameters derived from it. The measured depth of light penetration Dp varied by as much as 7x between participants, while the critical radiant exposure for gelation Ec varied by as much as 70x. This significant scatter is attributed to a lack of common procedure, variation in light engines, epistemic uncertainties from the Jacobs equation, and the use of measurement tools with insufficient precision. The ILS findings highlight an urgent need for procedural standardization and better hardware characterization in this rapidly growing field.

Machine-Guided Discovery of Acrylate Photopolymer Compositions.

Additive manufacturing (AM) can be advanced by the diverse characteristics offered by thermoplastic and thermoset polymers and the further benefits of copolymerization. However, the availability of suitable polymeric materials for AM is limited and may not always be ideal for specific applications. Additionally, the extensive number of potential monomers and their combinations make experimental determination of resin compositions extremely time-consuming and costly. To overcome these challenges, we develop an active learning (AL) approach to effectively choose compositions in a ternary monomer space ranging from rigid to elastomeric. Our AL algorithm dynamically suggests monomer composition ratios for the subsequent round of testing, allowing us to efficiently build a robust machine learning (ML) model capable of predicting polymer properties, including Young's modulus, peak stress, ultimate strain, and Shore A hardness based on composition while minimizing the number of experiments. As a demonstration of the effectiveness of our approach, we use the ML model to drive material selection for a specific property, namely, Young's modulus. The results indicate that the ML model can be used to select material compositions within at least 10% of a targeted value of Young's modulus. We then use the materials designed by the ML model to 3D print a multimaterial "hand" with soft "skin" and rigid "bones". This work presents a promising tool for enabling informed AM material selection tailored to user specifications and accelerating material discovery using a limited monomer space.

[Cryotherapy combined with local spraying of isoniazid for the treatment of nasopharyngeal tuberculosis: a case report].

Nasopharyngeal tuberculosis refers to the tuberculosis in the nasopharynx, which is mainly treated with systemic chemotherapy with anti-tuberculosis drugs. Here, we reported a case of nasopharyngeal tuberculosis treated by cryosurgery combined with local spraying of isoniazid on the basis of systemic chemotherapy with anti-tuberculosis drugs. By reviewing the case data and relevant literature, we understood the clinical manifestations, diagnosis and differential diagnosis of the disease, improved everyone's understanding of the disease, and proposed a new method of cryosurgery combined with local spraying of isoniazid for the treatment of nasopharyngeal tuberculosis for clinical discussion.

Chemical Circularity in 3D Printing with Biobased Δ-Valerolactone.

Digital Light Processing (DLP) is a vat photopolymerization-based 3D printing technology that fabricates parts typically made of chemically crosslinked polymers. The rapidly growing DLP market has an increasing demand for polymer raw materials, along with growing environmental concerns. Therefore, circular DLP printing with a closed-loop recyclable ink is of great importance for sustainability. The low-ceiling temperature alkyl-substituted δ-valerolactone (VL) is an industrially accessible biorenewable feedstock for developing recyclable polymers. In this work, acrylate-functionalized poly(δ-valerolactone) (PVLA), synthesized through the ring-opening transesterification polymerization of VL, is used as a platform photoprecursor to improve the chemical circularity in DLP printing. A small portion of photocurable reactive diluent (RD) turns the unprintable PVLA into DLP printable ink. Various photocurable monomers can serve as RDs to modulate the properties of printed structures for applications like sacrificial molds, soft actuators, sensors, etc. The intrinsic depolymerizability of PVLA is well preserved, regardless of whether the printed polymer is a thermoplastic or thermoset. The recovery yield of virgin quality VL monomer is 93% through direct bulk thermolysis of the printed structures. This work proposes the utilization of depolymerizable photoprecursors and highlights the feasibility of biorenewable VL as a versatile material platform toward circular DLP printing.

[Current status and perspectives of obstetrics in China: an insight from evidence-based medicine].

Nationwide epidemiological data on pregnancy and childbirth was lacking in China during the last decades. Since the establishment of the national data monitoring network and the booming of regional epidemiological studies, more information has emerged. The results and suggestions from the China Labor and Delivery Survey are crucial for improving current clinical practices. Researches that are conducted based on national data monitoring network and hospital-based medical history system, and multicenter clinical trials will provide plenty of useful evidences. These high-quality evidences would further improve clinical practice and development of obstetrics in China.

Self-Healable and 4D Printable Hydrogel for Stretchable Electronics.

Materials with high stretchability and conductivity are used to fabricate stretchable electronics. Self-healing capability and four-dimensional (4D) printability are becoming increasingly important for these materials to facilitate their recovery from damage and endow them with stimuli-response properties. However, it remains challenging to design a single material that combines these four strengths. Here, a dually crosslinked hydrogel is developed by combining a covalently crosslinked acrylic acid (AAC) network and Fe3+ ions through dynamic and reversible ionically crosslinked coordination. The remarkable electrical sensitivity (a gauge factor of 3.93 under a strain of 1500%), superior stretchability (a fracture strain up to 1700%), self-healing ability (a healing efficiency of 88% and 97% for the mechanical and electrical properties, respectively), and 4D printability of the hydrogel are demonstrated by constructing a strain sensor, a two-dimensional touch panel, and shape-morphing structures with water-responsive behavior. The hydrogel demonstrates vast potential for applications in stretchable electronics.

[A case of fungal keratitis caused by Petriella setifera infection].

The patient, a 66-year-old male, suffered from redness, blurred vision, photophobia, and tearing in the right eye after being injured by a wooden board. Anti-inflammatory treatment showed poor effectiveness. A 4 mm × 4 mm infiltrate with white deposits on the surface was observed in the central cornea of the right eye. Microscopic examination of corneal scrapings, fungal culture, and in vivo confocal microscopy all indicated fungal infection. The isolated strain was identified as Scedosporium apiospermum through microscopic morphology and confirmed as Petriella setifera by gene sequencing. The patient received corneal debridement combined with routine anti-inflammatory and antifungal treatment in the outpatient clinic. During the follow-up period, the condition continued to improve. Slit lamp examination at the revisit 40 days after the initial diagnosis revealed thinning of the corneal stroma, basic healing of the epithelium, and an increase in uncorrected visual acuity from 0.3 to 0.6.

Active Materials for Functional Origami.

In recent decades, origami has been explored to aid in the design of engineering structures. These structures span multiple scales and have been demonstrated to be used toward various areas such as aerospace, metamaterial, biomedical, robotics, and architectural applications. Conventionally, origami or deployable structures have been actuated by hands, motors, or pneumatic actuators, which can result in heavy or bulky structures. On the other hand, active materials, which reconfigure in response to external stimulus, eliminate the need for external mechanical loads and bulky actuation systems. Thus, in recent years, active materials incorporated with deployable structures have shown promise for remote actuation of light weight, programmable origami. In this review, active materials such as shape memory polymers (SMPs) and alloys (SMAs), hydrogels, liquid crystal elastomers (LCEs), magnetic soft materials (MSMs), and covalent adaptable network (CAN) polymers, their actuation mechanisms, as well as how they have been utilized for active origami and where these structures are applicable is discussed. Additionally, the state-of-the-art fabrication methods to construct active origami are highlighted. The existing structural modeling strategies for origami, the constitutive models used to describe active materials, and the largest challenges and future directions for active origami research are summarized.

[Research advances on size selection and vault prediction of posterior chamber phakic intraocular lens].

Posterior chamber phakic intraocular lens (pIOL) implantation has been widely adopted for the correction of refractive errors. Among pIOLs, the Implantable Collamer Lens is the most common choice. The selection of the appropriate pIOL size and achieving the desired postoperative vault to minimize complications has consistently been a focal point in academic research. With the advancement of ophthalmic biometric measurement technology and the application of artificial intelligence in the field of medicine, numerous new technologies and methods for pIOL size selection and vault prediction have emerged in recent years. This paper provides a comprehensive review on the topic of how to choose the pIOL size and predict the vault.

3D Printed Conformal Strain and Humidity Sensors for Human Motion Prediction and Health Monitoring via Machine Learning.

Wearable sensors have garnered considerable attention due to their flexibility and lightweight characteristics in the realm of healthcare applications. However, developing robust wearable sensors with facile fabrication and good conformity remains a challenge. In this study, a conductive graphene nanoplate-carbon nanotube (GC) ink is synthesized for multi jet fusion (MJF) printing. The layer-by-layer fabrication process of MJF not only improves the mechanical and flame-retardant properties of the printed GC sensor but also bolsters its robustness and sensitivity. The direction of sensor bending significantly impacts the relative resistance changes, allowing for precise investigations of joint motions in the human body, such as those of the fingers, wrists, elbows, necks, and knees. Furthermore, the data of resistance changes collected by the GC sensor are utilized to train a support vector machine with a 95.83% accuracy rate for predicting human motions. Due to its stable humidity sensitivity, the sensor also demonstrates excellent performance in monitoring human breath and predicting breath modes (normal, fast, and deep breath), thereby expanding its potential applications in healthcare. This work opens up new avenues for using MJF-printed wearable sensors for a variety of healthcare applications.

Low-temperature 3D printing of transparent silica glass microstructures.

Transparent silica glass is one of the most essential materials used in society and industry, owing to its exceptional optical, thermal, and chemical properties. However, glass is extremely difficult to shape, especially into complex and miniaturized structures. Recent advances in three-dimensional (3D) printing have allowed for the creation of glass structures, but these methods involve time-consuming and high-temperature processes. Here, we report a photochemistry-based strategy for making glass structures of micrometer size under mild conditions. Our technique uses a photocurable polydimethylsiloxane resin that is 3D printed into complex structures and converted to silica glass via deep ultraviolet (DUV) irradiation in an ozone environment. The unique DUV-ozone conversion process for silica microstructures is low temperature (~220°C) and fast (<5 hours). The printed silica glass is highly transparent with smooth surface, comparable to commercial fused silica glass. This work enables the creation of arbitrary structures in silica glass through photochemistry and opens opportunities in unexplored territories for glass processing techniques.

Cold-programmed shape-morphing structures based on grayscale digital light processing 4D printing.

Shape-morphing structures that can reconfigure their shape to adapt to diverse tasks are highly desirable for intelligent machines in many interdisciplinary fields. Shape memory polymers are one of the most widely used stimuli-responsive materials, especially in 3D/4D printing, for fabricating shape-morphing systems. They typically go through a hot-programming step to obtain the shape-morphing capability, which possesses limited freedom of reconfigurability. Cold-programming, which directly deforms the structure into a temporary shape without increasing the temperature, is simple and more versatile but has stringent requirements on material properties. Here, we introduce grayscale digital light processing (g-DLP) based 3D printing as a simple and effective platform for fabricating shape-morphing structures with cold-programming capabilities. With the multimaterial-like printing capability of g-DLP, we develop heterogeneous hinge modules that can be cold-programmed by simply stretching at room temperature. Different configurations can be encoded during 3D printing with the variable distribution and direction of the modular-designed hinges. The hinge module allows controllable independent morphing enabled by cold programming. By leveraging the multimaterial-like printing capability, multi-shape morphing structures are presented. The g-DLP printing with cold-programming morphing strategy demonstrates enormous potential in the design and fabrication of shape-morphing structures.