Application of smart hydrogel materials in cartilage injury repair: A systematic review and meta-analysis.

OBJECTIVE: Cartilage injury is a common clinical condition, and treatment approaches have evolved over time from traditional conservative and surgical methods to regenerative repair. In this context, hydrogels, as widely used biomaterials in the field of cartilage repair, have garnered significant attention. Particularly, responsive hydrogels (also known as "smart hydrogels") have shown immense potential due to their ability to respond to various physicochemical properties and environmental changes. This paper aims to review the latest research developments of hydrogels in cartilage repair, utilizing a more systematic and comprehensive meta-analysis approach to evaluate the research status and application value of responsive hydrogels. The goal is to determine whether these materials demonstrate favorable therapeutic effects for subsequent clinical applications, thereby offering improved treatment methods for patients with cartilage injuries. METHOD: This study employed a systematic literature search method to summarize the research progress of responsive hydrogels by retrieving literature on the subject and review studies. The search terms included "hydrogel" and "cartilage," covering data from database inception up to October 2023. The quality of the literature was independently evaluated using Review Manager v5.4 software. Quantifiable data was statistically analyzed using the R language. RESULTS: A total of 7 articles were retrieved for further meta-analysis. In the quality assessment, the studies demonstrated reliability and accuracy. The results of the meta-analysis indicated that responsive hydrogels exhibit unique advantages and effective therapeutic outcomes in the field of cartilage repair. Subgroup analysis revealed potential influences of factors such as different types of hydrogels and animal models on treatment effects. CONCLUSION: Responsive hydrogels show significant therapeutic effects and substantial application potential in the field of cartilage repair. This study provides strong scientific evidence for their further clinical applications and research, with the hope of promoting advancements in the treatment of cartilage injuries.

Pluronic F127 hydrogel-loaded extracellular vesicles from adipose-derived mesenchymal stem cells promote tracheal cartilage regeneration via SCNN1B delivery.

Extracellular vesicles (EVs) derived from adipose-derived mesenchymal stem cells (AMSC-EVs) have been highlighted as a cell-free therapy due to their regenerative capability to enhance tissue and organ regeneration. Herein, we aimed to examine the mechanism of PF127-hydrogel@AMSC-EVs in promoting tracheal cartilage defect repair. Based on bioinformatics methods, SCNN1B was identified as a key gene for the osteogenic differentiation of AMSCs induced by AMSC-EVs. EVs were isolated from rat AMSCs and then loaded onto thermo-sensitive PF-127 hydrogel to develop PF127-hydrogel@AMSC-EVs. It was established that PF127-hydrogel@AMSC-EVs could effectively deliver SCNN1B into AMSCs, where SCNN1B promoted AMSC osteogenic differentiation. The promotive effect was evidenced by enhanced ALP activity, extracellular matrix mineralization, and expression of s-glycosaminoglycan, RUNX2, OCN, collagen II, PERK, and ATF4. Furthermore, the in vivo experiments revealed that PF127-hydrogel@AMSC-SCNN1B-EVs stimulated tracheal cartilage regeneration in rats through PERK/ATF4 signaling axis activation. Therefore, PF127-hydrogel@AMSC-SCNN1B-EVs may be a novel cell-free biomaterial to facilitate tracheal cartilage regeneration and cartilage injury repair.

Long noncoding RNA SNHG3 interacts with microRNA-502-3p to mediate ITGA6 expression in liver hepatocellular carcinoma.

SNHG3, a long noncoding RNA (lncRNA), has been linked to poor outcomes in patients with liver hepatocellular carcinoma (LIHC). In this study, we found that SNHG3 was overexpressed in LIHC and associated with poor outcomes in patients with LIHC. Functional assays, including colony formation, spheroid formation, and in vivo assays showed that SNHG3 promoted stemness of cancer stem cells (CSC) and tumor growth in vivo by interacting with microRNA-502-3p (miR-502-3p). miR-502-3p inhibitor repressed the tumor-suppressing effects of SNHG3 depletion. Finally, by RNA pull-down, dual-luciferase reporter assay, m6A methylation level detection, and m6A-IP-qPCR assays, we found that miR-502-3p targeted YTHDF3 to regulate the translation of integrin alpha-6 (ITGA6) and targeted HBXIP to inhibit the m6A modification of ITGA6 through methyltransferase-like 3 (METTL3). Our study revealed that SNHG3 controls the YTHDF3/ITGA6 and HBXIP/METTL3/ITGA6 pathways by repressing miR-502-3p expression to sustain the self-renewal properties of CSC in LIHC.

Semantic segmentation for tooth cracks using improved DeepLabv3+ model.

Objective: Accurate and prompt detection of cracked teeth plays a critical role for human oral health. The aim of this paper is to evaluate the performance of a tooth crack segmentation model (namely, FDB-DeepLabv3+) on optical microscopic images. Method: The FDB-DeepLabv3+ model proposed here improves feature learning by replacing the backbone with ResNet50. Feature pyramid network (FPN) is introduced to fuse muti-level features. Densely linked atrous spatial pyramid pooling (Dense ASPP) is applied to achieve denser pixel sampling and wider receptive field. Bottleneck attention module (BAM) is embedded to enhance local feature extraction. Results: Through testing on a self-made hidden cracked tooth dataset, the proposed method outperforms four classical networks (FCN, U-Net, SegNet, DeepLabv3+) on segmentation results in terms of mean pixel accuracy (MPA) and mean intersection over union (MIoU). The network achieves an increase of 11.41% in MPA and 12.14% in MIoU compared to DeepLabv3+. Ablation experiments shows that all the modifications are beneficial. Conclusion: An improved network is designed for segmenting tooth surface cracks with good overall performance and robustness, which may hold significant potential in computer-aided diagnosis of cracked teeth.

From molecular sieving to gas effusion through nanoporous 2D graphenes: Comparison between analytical predictions and molecular simulations.

In this paper, we study the permeation of polyatomic gas molecules through 2D graphene membranes. Using equilibrium molecular dynamics simulations, we investigate the permeation of pure gas compounds (CH4, CO2, O2, N2, and H2) through nanoporous graphene membranes with varying pore sizes and geometries. Our simulations consider the recrossing mechanism, often neglected in previous studies, which has a significant effect on permeation for intermediate pore size to molecular diameter ratios. We find that the permeation process can be decoupled into two steps: the crossing process of gas molecules through the pore plane and the escaping process from the pore region to a neighboring adsorption site, which prevents recrossing. To account for these mechanisms, we use a permeance model expressed as the product of the permeance for the crossing process and the probability of molecule escape. This phenomenological model is extended to account for small polyatomic gas molecules and to describe permeation regimes ranging from molecular sieving to effusion. The proposed model captures the temperature dependence and provides insights into the key parameters of the gas/membrane interaction controlling the permeance of the system. This work lays the foundation for predicting gas permeance and exploring membrane separation factors in 2D materials such as graphene.

Sources, distribution, and environmental effects of microplastics: a systematic review.

Microplastics (MPs) are receiving increasing attention from researchers. They are environmental pollutants that do not degrade easily, are retained for prolonged periods in environmental media such as water and sediments, and are known to accumulate in aquatic organisms. The aim of this review is to show and discuss the transport and effects of microplastics in the environment. We systematically and critically review 91 articles in the field of sources, distribution, and environmental behavior of microplastics. We conclude that the spread of plastic pollution is related to a myriad of processes and that both primary and secondary MPs are prevalent in the environment. Rivers have been indicated as major pathways for the transport of MPs from terrestrial areas into the ocean, and atmospheric circulation may be an important avenue for transporting MPs between environmental compartments. Additionally, the vector effect of MPs can change the original environmental behavior of other pollutants, leading to severe compound toxicity. Further in-depth studies on the distribution and chemical and biological interactions of MPs are highly suggested to improve our understanding of how MPs behave in the environment.

Ingestion of microplastics by silver carp (Hypophthalmichthys molitrix) larvae: Quantification of ingestion and assessment of microbiota dysbiosis.

The adverse effects of microplastics ingested by aquatic organisms have been reported previously. However, most studies are primarily qualitative; therefore, it is challenging to determine the direct interactions between microplastics and organisms. In this study, for the first time, the microplastic intake behavior of silver carp (Hypophthalmichthys molitrix) larvae, a popular fish in China, as well as intestine accumulation and excretion of the microplastics were quantitatively investigated. The results showed that the intake of microplastics by silver carp larvae was negatively correlated with the particle size of microplastics but positively correlated with the exposure concentration. After intaking microplastics of different sizes, small-sized microplastics (≤ 150 µm) were rapidly excreted from the intestine of silver carp, whereas some large-sized microplastics (≥ 300 µm) remained in the intestine for a long time. The presence of food significantly increased the intake of large-sized microplastics, while small-sized microplastics intake was unaffected by the food. More importantly, the ingested microplastics caused specific changes in the diversity of intestinal microflora, potentially leading to abnormal immune and metabolic functions. The results of this study provide a new understanding on the potential impacts of microplastics on aquatic organisms.

CXCL8 delivered by plasma-derived exosomes induces the symptoms of post-traumatic stress disorder through facilitating astrocyte-neuron communication.

Extracellular vesicles (EVs) play an important role in post-traumatic stress disorder (PTSD). This study is aimed to investigate the possible molecular mechanism of CD63 mediating CXCL8 delivery via EVs to affect astrocyte-neuron communication in PTSD. The neuron-derived EVs (NDEVs) and astrocyte-derived EVs (ADEVs) were isolated from plasma in PTSD patients. Next, the uptake of EVs by neurons was assessed. Following determination of the interaction between CD63 and CXCL8, gain- and loss-of-function experiments were performed in astrocytes. Finally, a PTSD mouse model was established using the single prolonged stress and electric foot shock to confirm the effects of plasma-derived EVs delivering CXCL8 on anxiety- and depression-like behaviors in PTSD mice. EVs derived from plasma of PTSD patients aggravated anxiety- and depression-like behaviors in PTSD mice. CXCL8 was a key gene upregulated in both NDEVs and ADEVs from plasma of PTSD patients, which could be delivered into EVs by CD63. Meanwhile, CXCL8 was also highly expressed in plasma-derived EVs. In vivo experiments also verified that plasma-derived EVs could enhance astrocyte-neuron communication by delivering CXCL8, and silencing of CXCL8 ameliorated anxiety- and depression-like behaviors in PTSD mice. Taken together, CD63 promotes delivery of CXCL8 via EVs to induce PTSD by enhancing astrocyte-neuron communication, suggesting the potential of CD63 mediating delivery of CXCL8 via EVs as a therapeutic target for PTSD.

Fetal Ultrasound Standard Plane Detection With Coarse-to-Fine Multi-Task Learning.

The ultrasound standard plane plays an important role in prenatal fetal growth parameter measurement and disease diagnosis in prenatal screening. However, obtaining standard planes in a fetal ultrasound video is not only laborious and time-consuming but also depends on the clinical experience of sonographers to a certain extent. To improve the acquisition efficiency and accuracy of the ultrasound standard plane, we propose a novel detection framework that utilizes both the coarse-to-fine detection strategy and multi-task learning mechanism for feature-fused images. First, traditional manually-designed features and deep learning-based features are fused to obtain low-level shared features, which can enhance the model's feature expression ability. Inspired by the process of human recognition, ultrasound standard plane detection is divided into a coarse process of plane type classification and a fine process of standard-or-not detection, which is implemented via an end-to-end multi-task learning network. The region-of-interest area is also recognised in our detection framework to suppress the influence of a variable maternal background. Extensive experiments are conducted on three ultrasound planes of the first-class fetal examination, i.e., the femur, thalamus, and abdomen ultrasound images. The experiment results show that our method outperforms competing methods in terms of accuracy, which demonstrates the efficacy of the proposed method and can reduce the workload of sonographers in prenatal screening.

Modeling gas permeation mechanisms through 2D membranes: Comparison between a phenomenological model and extensive molecular simulations.

Two-dimensional (2D) membranes based on perforated graphene have great potential in the field of separation of chemical species for a variety of applications, including gas treatment. In addition to recent experimental studies, several works simulate the mechanisms of gas permeation through this type of membrane using molecular dynamics, but few combine different techniques to ensure that their method of choice captures all relevant mechanisms. In particular, the re-crossing mechanism leading a gas molecule that has crossed the plane of the membrane to rapidly re-cross it in the opposite direction has never been documented. In this work, we study gas permeation through a simplified 2D membrane model. We combine equilibrium and non-equilibrium molecular dynamics simulations to quantify the impact of these re-crossing mechanisms on the values of the computed transport coefficients. Using non-equilibrium simulations as reference, we show that the equilibrium simulation techniques commonly used can lead to a significant overestimation of the transport properties of the membrane. We propose a simple method to probe the re-crossing dynamics during equilibrium simulations, making it possible to compute correct values of the transport coefficient without the need for non-equilibrium simulations. Furthermore, by analyzing the phenomenology observed in the simulations, we derive an analytical formula for the permeance that takes the form of an Arrhenius law with a non-trivial temperature dependent prefactor. In excellent agreement with our simulation results, this model provides a simple theoretical framework that captures the main mechanisms involved in gas permeation through 2D membranes, including the effect of re-crossing.

Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks.

Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates, 10-5 is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with 1920 × 1080-pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.

Molecular and serological detection of Toxoplasma gondii infection in mammals in the Taipei Zoo.

Toxoplasmosis caused by Toxoplasma gondii affects both conservation and public health efforts. In the Taipei Zoo, toxoplasmosis was diagnosed in ring-tailed lemurs and a meerkat in 2019 while a freeze-thaw meat strategy had been applied to carnivores before the event. To investigate the possible risk factors associated with T. gondii infection in the Taipei Zoo, 179 veterinary visiting mammals from 2019-2021 and six stray cats were included to detect anti-T. gondii IgG and IgM in their serum via ELISA, and T. gondii in their faeces and blood via PCR. Although the overall T. gondii IgG seroprevalence was 33.5% and PCR positivity was 16.2% in the zoo mammals, the correlation between T. gondii PCR and systemic IgG results was low. An omnivorous diet (adjusted OR = 0.4; 95% CI: 0.2-1.0), a herbivorous diet (adjusted OR = 3.2; 95% CI: 1.1-9.6), and animals in the Conservation Area where stray cats appeared (adjusted OR = 18.3; 95% CI: 3.9-85.9) were independent risk factors for T. gondii infection. The low T. gondii-specific IgM positivity (0.6%) suggests that most animals did not have acute T. gondii infection. In conclusion, our findings indirectly support that feeding frozen meat to carnivores, cleaning fresh food, and restricting access to stray cats to prevent faecal contaminants could prevent animals from T. gondii exposure.

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis.

Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What's more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.

"Phoenix in Flight": an unique fruit morphology ensures wind dispersal of seeds of the phoenix tree (Firmiana simplex (L.) W. Wight).

BACKGROUND: Many seed plants produce winged diaspores that use wind to disperse their seeds. The morphology of these diaspores is directly related to the seed dispersal potential. The majority of winged diaspores have flat wings and only seeds; however, some angiosperms, such as Firmiana produce winged fruit with a different morphology, whose seed dispersal mechanisms are not yet fully understood. In this study, we observed the fruit development of F. simplex and determined the morphological characteristics of mature fruit and their effects on the flight performance of the fruit. RESULTS: We found that the pericarp of F. simplex dehisced early and continued to unfold and expand during fruit development until ripening, finally formed a spoon-shaped wing with multiple alternate seeds on each edge. The wing caused mature fruit to spin stably during descent to provide a low terminal velocity, which was correlated with the wing loading and the distribution of seeds on the pericarp. When the curvature distribution of the pericarp surface substantially changed, the aerodynamic characteristics of fruit during descent altered, resulting in the inability of the fruit to spin. CONCLUSIONS: Our results suggest that the curved shape and alternate seed distribution are necessary for the winged diaspore of F. simplex to stabilize spinning during wind dispersal. These unique morphological characteristics are related to the early cracking of fruits during development, which may be an adaptation for the wind dispersal of seeds.

A deep learning method for the recognition of solar radio burst spectrum.

Solar radiation is the excitation source that affects the weather in the atmosphere of the earth, and some solar activities such as flares and coronal mass ejections are often accompanied by radio bursts. The spectrum of solar radio bursts is helpful for astronomers to explore the mechanism of radio bursts. With the development and progress of solar radio spectrum observation methods, the observation of the Sun can be done at almost all times of day. How to quickly and automatically identify the small proportion of burst data from the huge corpus of observation data has become an important research direction. The innovation of this study is to enhance the original radio spectrum dataset with unbalanced sample distribution, and a neural network model for solar radio spectrum image classification is proposed on this basis. This hybrid structure of joint convolution and a memory unit overcomes the shortcoming of the traditional convolution or memory model, which can only extract one-sided features of an image. By extracting the frequency structure features and time-series features at the same time, the sensitivity to the small features of the spectrum image can be enhanced. Based on the data of the Solar Broadband Radio Spectrometer (SBRS) in China, the proposed network model can improve the average classification accuracy of the spectrum image to 98.73%, which will be helpful for related astronomical research.

A method of crack detection based on digital image correlation for simulated cracked tooth.

BACKGROUND: Early clinical cracked tooth can be a perplexing disorder to diagnose and manage. One of the key problems for the diagnosis of the cracked tooth is the detection of the location of the surface crack. METHODS: This paper proposes an image-based method for the detection of the micro-crack in the simulated cracked tooth. A homemade three-axis motion platform mounted with a telecentric lens was built as an image acquisition system to observe the surface of the simulated cracked tooth, which was under compression with a magnitude of the masticatory force. By using digital image correlation (DIC), the deformation map for the crown surface of the cracked tooth was calculated. Through image analysis, the micro-crack was quantitatively visualized and characterized. RESULTS: The skeleton of the crack path was successfully extracted from the image of the principal strain field, which was further verified by the image from micro-CT. Based on crack kinematics, the crack opening displacement was quantitatively calculated to be 2-10 µm under the normal mastication stress, which was in good agreement with the value reported in the literature. CONCLUSIONS: The crack on the surface of the simulated cracked tooth could be detected based on the proposed DIC-based method. The proposed method may provide a new solution for the rapid clinical diagnosis of cracked teeth and the calculated crack information would be helpful for the subsequent clinical treatment of cracked teeth.

Three-dimensional Dirac material anode enables concentrated solar thermionic converters.

Concentrated solar thermionic converters (CSTCs) are proposed by using three-dimensional (3D) Dirac material (DM) as the novel anode, significantly improving device performance. A theoretical model is developed to investigate the optimal performance of CSTCs. Under a solar concentration of 500, the CSTC reaches a maximum conversion efficiency of 11.8%. Furthermore, the optimal working condition and parametric selection criteria of the CSTC parameters under different solar concentrations are determined. Importantly, we demonstrate that 3D DM as an anode has more advantages over metal and graphene in CSTCs. This work thus offers a theoretical foundation for the exploration of solar thermionic application in using 3D DMs as novel electrodes.

Investigation of Lethal Concurrent Outbreak of Chlamydiosis and Pigeon Circovirus in a Zoo.

During the spring, an outbreak of sudden death involving 58 birds occurred in a zoo. Histopathological examinations revealed variable numbers of intracytoplasmic basophilic microorganisms in the macrophages, hepatocytes, and renal epithelium of most birds, along with occasional botryoid intracytoplasmic inclusion bodies within histiocytes in the bursa of Fabricius. Based on the results of histopathological examinations, immunohistochemical staining, transmission electron microscopy, and polymerase chain reactions, genotype B Chlamydia psittaci infection concurrent with pigeon circovirus (PiCV) was diagnosed. A retrospective survey, including two years before the outbreak and the outbreak year, of C. psittaci and PiCV infections of dead birds in the aviaries, revealed that the outbreak was an independent episode. The findings of this study indicate that concurrent infection with C. psittaci and PiCV might lead to lethal outbreaks of chlamydiosis, particularly Streptopelia orientalis. In addition, persistently monitoring both pathogens and identifying potential PiCV carriers or transmitters might also help prevent lethal disease outbreaks.

KDM5B promotes self-renewal of hepatocellular carcinoma cells through the microRNA-448-mediated YTHDF3/ITGA6 axis.

Histone methylation plays important roles in mediating the onset and progression of various cancers, and lysine-specific demethylase 5B (KDM5B), as a histone demethylase, is reported to be an oncogene in hepatocellular carcinoma (HCC). However, the mechanism underlying its tumorigenesis remains undefined. Hence, we explored the regulatory role of KDM5B in HCC cells, aiming to identify novel therapeutic targets for HCC. Gene Expression Omnibus database and StarBase were used to predict important regulatory pathways related to HCC. Then, the expression of KDM5B and microRNA-448 (miR-448) in HCC tissues was detected by RT-qPCR and Western blot analysis. The correlation between KDM5B and miR-448 expression was analysed by Pearson's correlation coefficient and ChIP experiments, and the targeting of YTH N6-methyladenosine RNA binding protein 3 (YTHDF3) by miR-448 was examined by luciferase assay. Additionally, the effect of KDM5B on the proliferation, migration, invasion and apoptosis as well as tumorigenicity of transfected cells was assessed using ectopic expression and depletion experiments. KDM5B was highly expressed in HCC cells and was inversely related to miR-448 expression. KDM5B demethylated H3K4me3 on the miR-448 promoter and thereby inhibited the expression of miR-448, which in turn targeted YTHDF3 and integrin subunit alpha 6 (ITGA6) to promote the malignant phenotype of HCC. Moreover, KDM5B accelerated HCC progression in nude mice via the miR-448/YTHDF3/ITGA6 axis. Our study uncovered that KDM5B regulates the YTHDF3/ITGA6 axis by inhibiting the expression of miR-448 to promote the occurrence of HCC.

Comparative Assessment of Various Low-Dissipation Combined Models for Three-Terminal Heat Pump Systems.

Thermally driven heat pump systems play important roles in the utilization of low-grade thermal energy. In order to evaluate and compare the performances of three different constructions of thermally driven heat pump and heat transformer, the low-dissipation assumption has been adopted to establish the irreversible thermodynamic models of them in the present paper. By means of the proposed models, the heating loads, the coefficients of performance (COPs) and the optimal relations between them for various constructions are derived and discussed. The performances of different constructions are numerically assessed. More importantly, according to the results obtained, the upper and lower bounds of the COP at maximum heating load for different constructions are generated and compared by the introduction of a parameter measuring the deviation from the reversible limit of the system. Accordingly, the optimal constructions for the low-dissipation three-terminal heat pump and heat transformer are determined within the frame of low-dissipation assumption, respectively. The optimal constructions in accord with previous research and engineering practices for various three-terminal devices are obtained, which confirms the compatibility between the low-dissipation model and endoreversible model and highlights the validity of the application of low-dissipation model for multi-terminal thermodynamic devices. The proposed models and the significant results obtained enrich the theoretical thermodynamic model of thermally driven heat pump systems and may provide some useful guidelines for the design and operation of realistic thermally driven heat pump systems.