Developing a novel strategy for fabricating matrix film to assess the distribution of potassium perfluorooctanic sulfonate by matrix-assisted laser desorption/ionization mass spectrometry imaging.

Matrix deposition plays a critical role in image quality of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). To improve the ionization efficiency and overcome the limitation of traditional matrix deposition methods in the face of difficult-to-sublimate or difficult-to-dissolve matrix, covalent organic frameworks (COFs) named COF-DhaTab was successfully synthesized and firstly used as matrix film. It was fabricated by imprinting of sieved COF-DhaTab powder on the surface of a double-sided adhesive tape. Outstanding reproducibility and uniformity of COF-DhaTab film were demonstrated by relative standard deviation (RSD) within 8.37% and 7.71% from dot-to-dot and plate-to-plate, respectively. With the introduction of double-sided adhesive tape, water contact angle (WCA) of COF-DhaTab film increased from 55° to 141°, resulting in significant suppression of analyte diffusion. Moreover, the intensity of potassium perfluorooctanic sulfonate (PFOS, C8F17SO3-, m/z 498.93) was 9.3 × 105, more than six hundred times higher than that using DHB matrix. This enhancement was attributed to the rough surface and multiple branches of the synthesized COF-DhaTab. To verify the ability of COF-DhaTab film as substrate, the spatial distribution of PFOS in zebrafish, rat liver and kidney tissues was explored. Superior imaging capability was displayed with high-spatial resolution and reliable location distribution. These results not only demonstrate the outstanding ability of COF-DhaTab as matrix for MALDI-MS and MALDI-MSI, but also provide a facile approach for fabrication of novel matrix films for MALDI-MSI.

Twice-walk strategy based on three-dimensional DNA walking machine driven by duplex-specific nuclease.

A twice-walk strategy based on a three-dimensional (3D) cleat-equipped DNA walking machine with a high signal amplification efficiency was investigated for ultrasensitive detection of miRNA. Impressively, addition of duplex-specific nuclease (DSN) just once drove the twice-walk strategy, making the strategy simpler. With the advantages of being simple, rapid and ultrasensitive, the biosensor offers potential for use in early clinical diagnosis.

Self-aspiration sampling design for rapid analyses of volatile organic compounds based on atmospheric pressure chemical ionization/photoionization combined ionization source mass spectrometry.

Development of combined mass spectrometry ionization sources has enabled expansion of the application and scope of mass spectrometry. A novel hybrid ionization system combining vacuum ultraviolet (VUV) and atmospheric pressure chemical ionization (APCI) was constructed. Gaseous samples were self-aspirated into an ionization zone through a capillary by negative pressure, generated by high-speed airflow based on the Venturi effect. Compared with APCI mode alone, the signal-to-noise ratio (S/N) in APCI/VUV mode was increased by about 276-times. To increase the ionization efficiency further, correlated experimental conditions were optimized. Four types of volatile organic compounds (VOCs) were tested to evaluate the performance of the APCI/VUV ion source. Excellent linearity and limit of detection were achieved for compounds in mixed solutions. Quantitative analyses of four VOCs (toluene, cyclohexanone, styrene and ethylbenzene) using APCI/VUV-MS were done, and the relative standard deviations (RSDs) were 1.57%, 6.30%, 4.49% and 8.21%, respectively, indicating that the APCI/VUV ionization source had excellent reproducibility. Our results demonstrated that the developed method was promising for analyzing VOCs as well as being rapid, simple, and easy to operate.

Layperson assessment of smile lines and upper lip combined images in smile esthetics.

STATEMENT OF PROBLEM: The esthetic assessment of smile lines by laypersons is a subject of ongoing debate. However, smile lines often appear with different types of upper lip curvature, which further complicates the esthetic assessment process, and studies on this combination are lacking. PURPOSE: The purpose of this clinical study was to investigate a layperson's esthetic perception of smile lines and upper lip combined images. MATERIAL AND METHODS: Twenty-six smile images resulting from combinations of 3 upper lip types, 4 anterior smile line types, and 3 posterior smile line types were generated by an image editing software program. Eighty-three laypersons (39 men and 44 women; 18 to 35 years of age) completed rating images using a visual analog scale. Unattractive smiles were designated to be those with scores <50 and attractive ones with scores ≥50. Data were analyzed using 1-way analysis of variance and Bonferroni post hoc tests (α=.05). RESULTS: High anterior smile line with gingival display >4 mm obtained significantly lower scores of <50 when combined with all upper lip curvatures (upward: 28.29 ±22.79, straight: 38.74 ±23.00, downward: 30.67 ±22.25, P<.01). High anterior smile lines with gingival display ≤4 mm combined with upward and straight upper lip curvature images obtained significantly higher scores, and all were ≥50 (upward: 63.24 ±22.22, straight: 61.40 ±21.58, P<.01). CONCLUSIONS: From a layperson's perspective, high anterior smile lines with gingival display >4 mm combined with any lip type were determined to be unattractive. If gingival display was ≤4 mm combined with both upward and straight lip types, the smile was assessed as attractive.

A rapid one-step electrochemical method based on cleat-equipped molecular walking machine.

Various nucleic acid molecular machines have emerged in recent years. However, when the nucleic acid tracks are fully depleted, these walkers are highly susceptible to premature release or stalling in regions where the tracks are locally exhausted. In this work, a molecular walking machine with a cleat domain preventing dissociation from the track was explored for ultrasensitive detection of miRNA. It has been verified that the cleat design can enhance the signal amplification efficiency of molecular walking machines for electrochemical miRNA-141 detection. Notably, the single-step electrochemical biosensing platform utilizing the cleat-equipped molecular walking machine (CMWM) is exceptionally straightforward and rapid, concluding the reaction within 90 min and achieving a remarkable low detection limit of 0.26 fM. The proposed molecular walking machine with this specific cleat structure was utilized for the identification of miRNA-141 in cellular lysates, exhibiting remarkable selectivity and consistent reproducibility, showcasing its effective utility in bioanalysis. Therefore, the cleat walker developed in this study introduces an innovative method for constructing a miRNA electrochemical biosensing platform, offering new perspectives for its application in biomolecule detection and clinical disease diagnosis.

Highly Flexible, High-Performance, and Stretchable Piezoelectric Sensor Based on a Hierarchical Droplet-Shaped Ceramics with Enhanced Damage Tolerance.

Stretchable self-powered sensors are of significant interest in next-generation wearable electronics. However, current strategies for creating stretchable piezoelectric sensors based on piezoelectric polymers or 0-3 piezoelectric composites face several challenges such as low piezoelectric activity, low sensitivity, and poor durability. In this paper, a biomimetic soft-rigid hybrid strategy is used to construct a new form of highly flexible, high-performance, and stretchable piezoelectric sensor. Inspired by the hinged bivalve Cristaria plicata, hierarchical droplet-shaped ceramics are manufactured and used as rigid components, where computational models indicate that the unique arched curved surface and rounded corners of this bionic structure can alleviate stress concentrations. To ensure electrical connectivity of the piezoelectric phase during stretching, a patterned liquid metal acts as a soft circuit and a silicone polymer with optimized wettability and stretchability serves as a soft component that forms a strong mechanical interlock with the hierarchical ceramics. The novel sensor design exhibits excellent sensitivity and durability, where the open circuit voltage remains stable after 5000 stretching cycles at 60% strain and 5000 twisting cycles at 180°. To demonstrate its potential in heathcare applications, this new stretchable sensor is successfully used for wireless gesture recognition and assessing the progression of knee osteoarthritis.

Analysis of liquid-vapor mixed migration mechanism in unsaturated soil based on the effect of temperature on soil microstructure.

Moisture migration in unsaturated soils is a result of the interaction between temperature and soil microstructure. In order to reveal the mechanism of moisture increase of subgrade soils under diurnal cycle conditions, a series of macro and microscopic tests were carried out on the unsaturated silty clay and sand soil, including liquid-vapor mixed migration tests simulating a one-dimensional subgrade, environmental scanning electron microscope (ESEM), and matrix suction test. Then, the soil microstructure in microscopic images was investigated using the particle (pores) and cracks analysis system (PACS). Next, the relationship between the thermal effects of the soil-water characteristic curve (SWCC) and changes in soil microstructure was analyzed. Finally, the change mechanism of liquid-vapor mixed migration based on the change in soil microstructural under thermal effects was analyzed. The results showed under the diurnal cycle, both the silty clay and sand soil columns appeared in the phenomenon of a "diurnal cycle of water vapor migration", which led to moisture accumulation at the top of the soil layer. In silty clay soil column, moisture was primarily driven by water vapor pressure and migrated upwards. Additionally, moisture redistribution led to changes in soil microstructure, which in turn influenced the process of moisture migration. The moisture content in the upper soil layer increased making both inter-aggregate and intra-aggregate pores decrease. The moisture content in the lower soil later decreased, leading to the water-holding capacity of the lower soil layer to increase. So, the moisture migration gradually decreased at night. In the sand soil column, moisture migration was mainly driven by gravity potential and migrated downwards. Moisture redistribution made inter-aggregate pore and matrix suction of the upper soil layer increase, leading to an increase in moisture migration at night.

Rapid and sensitive determination of free fatty acids based on in-source microdroplet-driven derivatization coupled with high-resolution mass spectrometry.

Accurate and sensitive measurements of free fatty acids (FFAs) in biological samples are valuable for diagnosing and prognosing diseases. In this study, an in-source microdroplet derivation strategy combined with high-resolution mass spectrometry was developed to analyze FFAs in lipid extracts of biological samples directly. FFAs were rapidly derivated with 2-picolylamine (PA) in the microdroplet which is derived by electrospray. With the proposed method, twelve typical FFAs were determined reliably with high sensitivity and acceptable linearities (R2 ≥ 0.94). The LODs and LOQs for the twelve FFAs were 9-76 pg mL-1 and 30-253 pg mL-1, respectively. The developed method was applied to analyze the alteration of FFAs in liver and kidney samples of rats induced by perfluorooctane sulfonate (PFOS) exposure. The good results demonstrate that the established analysis technique is dependable and has promising applications in detecting FFAs associated with complex biological samples.

Large Deflection of Foam-Filled Triangular Tubes under Transverse Loading.

In this paper, the large deflection of the foam-filled triangular tube (FFTT) is studied analytically and numerically under transverse loading. Considering the strengths of the foam and the tube, the yield criterion of FFTT is established. Based on the yield criterion, a theoretical model for the large deflection of the clamped triangular tube filled with foam under transverse loading is developed. The numerical simulations are carried out using ABAQUS/Standard software, and the analytical results are compared with the numerical ones. The effects of foam strength, thickness of the tube, and the width of the punch on the load-bearing capacity and energy absorption of the clamped FFTT loaded transversally are discussed in detail. It is demonstrated that the load-bearing ability and the energy absorption increase with increasing foam strength, tube thickness, and punch width. The closer the loading position is to the clamped end, the greater the increases in the capacity of load bearing and the energy absorption of the triangular tube filled with foam. The theoretical model can be used to foresee the large deflection of metal FFTT under transverse loading.

Controlled release of curcumin from gelatin hydrogels by the molecular-weight modulation of an oxidized dextran cross-linker.

Controlled drug delivery could minimize side effects while maintaining a high local dose. Herein, a hydrogel carrier was prepared by forming dynamic imine bonds between gelatin and oxidized dextran (ODex) of different molecular weights (Mw = 10, 70, and 150 kDa). The morphology, thermal stability, rheology, mechanical properties, and swelling properties of the hydrogels and the controlled release of curcumin were characterized. When dextran with a higher Mw was used, the ODex contained more aldehyde groups, which led to a higher degree of cross-linking, considerably shorter gel time, decreased hydrogel porosity, and well-controlled release of curcumin. In addition, the cross-linked hydrogels exhibited not only high thermal stability but also excellent mechanical properties. However, because the matrix was hydrophilic, the swelling properties of the hydrogels were not significantly affected by the Mw of ODex. These observations suggest an approach for designing nutrient delivery carriers with improved controlled release.

Efficiency and Potential Evaluation to Promote Differentiated Low-Carbon Management in Chinese Counties.

Low-carbon management plays an important role in mitigating climate change and adapting to it. Localities should adopt differentiated low-carbon management policies according to the state of their environment. To help formulate specific and realistic low-carbon management policies, this paper took into account specific low-carbon management sectors. Likewise, it carefully considered the differences in various resource endowments and proposed a method for evaluating low-carbon management efficiency and potential. The method was applied to an empirical study from 2015 conducted on 1771 Chinese counties. Significant spatial heterogeneity was found during the research. The counties bordering central and Western China and the ones in the southeast coastal areas showed higher efficiency in the industrial sector. Southern and Northern China had higher efficiency in the housing and transportation sector, respectively. Moreover, counties in remote areas showed more potential in the industrial sector. Central China had higher potential in the housing sector, while counties bordering provinces had more potential in the transportation sector. Therefore, Chinese counties were divided into eight management zones where differentiated management strategies were identified to shape low-carbon management policies.

AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images.

Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.

Surgical Tool Handle Vibration-Based Drilling State Recognition During Hip Fracture Fixation.

OBJECTIVES: Traditional manual drilling during hip fracture fixation can easily lead to unstable fixation and vascular damage. This study aimed to investigate a safe and easy-to-use robot-assisted method to automatically drill bone and distinguish critical bone drilling states with high accuracy in real-time for the bone hole-making process during hip fracture fixation. METHODS: A bone-drilling robotic system was designed to automatically create holes in the femoral neck. Four fresh pig femurs were drilled at the posterosuperior femoral neck using three modes: "all-in" (AI), "in-out-in" (IOI), and "percutaneous fixation" (PF). A high-frequency accelerometer captured the generated vibrations of the drill handle, which were then transferred to a personal computer using a data acquisition card. Five bone drilling states are defined, including: "drill idling," "initial drilling," "in the cancellous bone," "out the femoral neck," and "in the cortical bone." The harmonic distribution of the vibration signal was extracted by fast Fourier transform (FFT) and used as a critical feature to identify different drilling states. To prove the difference in the harmonic distribution at different drilling states, an independent sample t-test was used to compare the percentage of the first harmonic amplitude in the first 10 harmonics at each drilling state. A neural network classifier was trained with the frequency spectrum as the input and the drilled state as the output to distinguish the critical bone drilling states with high accuracy in real-time. The classifier was trained and tested on four specimens to ensure that the surgical robot could accurately identify the five drilling states. RESULTS: In each specimen, the harmonic distributions of the drilling vibration at different drilling modes were significantly different (p < 0.05). The average recognition accuracies of the drilling state for the four specimens were all higher than 84%. The three defined modes were distinguished with extremely high accuracies. The recognition accuracies of "in the cancellous bone" for specimens 1 to 4 were 83.2%, 84.8%, 92.9%, and 84.7%. The recognition accuracies of "in out the femoral neck" from specimens 1 to 4 are 98.2%, 88.4%, 95.8%, and 88.8%. The recognition accuracies of "in the cortical bone" for specimens 1 to 4 were 94.6%, 80.8%, 95.5%, and 85.8%. CONCLUSIONS: The proposed robot-assisted method can automatically distinguish five critical bone-drilling states with high accuracy in real-time to avoid weak fixation and damage to the lateral epiphyseal artery.

C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation.

BACKGROUND AND OBJECTIVE: Breast lesions segmentation is an important step of computer-aided diagnosis system. However, speckle noise, heterogeneous structure, and similar intensity distributions bring challenges for breast lesion segmentation. METHODS: In this paper, we presented a novel cascaded convolutional neural network integrating U-net, bidirectional attention guidance network (BAGNet) and refinement residual network (RFNet) for the lesion segmentation in breast ultrasound images. Specifically, we first use U-net to generate a set of saliency maps containing low-level and high-level image structures. Then, the bidirectional attention guidance network is used to capture the context between global (low-level) and local (high-level) features from the saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue on the lesion regions. Furthermore, we developed a refinement residual network based on the core architecture of U-net to learn the difference between rough saliency feature maps and ground-truth masks. The learning of residuals can assist us to obtain a more complete lesion mask. RESULTS: To evaluate the segmentation performance of the network, we compared with several state-of-the-art segmentation methods on the public breast ultrasound dataset (BUSIS) using six commonly used evaluation metrics. Our method achieves the highest scores on six metrics. Furthermore, p-values indicate significant differences between our method and the comparative methods. CONCLUSIONS: Experimental results show that our method achieves the most competitive segmentation results. In addition, we apply the network on renal ultrasound images segmentation. In general, our method has good adaptability and robustness on ultrasound image segmentation.

Effect of automatic emotional processing on response inhibition among heroin abstainers.

Previous studies have explored effects of the conscious processing of emotional information on the interaction between emotion and executive control. However, the conscious processing of emotional information introduces "defensive responses," which might limit the emotional effect. To avoid the limitations, this study aimed to explore the effect of automatic emotional processing on response inhibition among heroin abstainers. Individuals experiencing heroin withdrawal symptoms are not entirely exempt from the influence of drugs, and it is challenging to find heroin addicts in practice. Therefore, similar to previous studies, we recruited heroin abstainers in this study. In Experiment 1, for the heroin abstainers group, 30 participants meeting the inclusion criteria were recruited from a compulsory isolated detoxification center, whereas 30 age- and education-matched participants were recruited from the community as the control group. These participants completed the Go/No-Go task by supraliminal emotional priming. In Experiment 2, the heroin abstainers group comprised 33 eligible participants and control group comprised 33 eligible participants. These participants completed the Go/No-Go task by subliminal emotional priming. We compared the participants' rates of commission errors in the Go/No-Go tasks. The rate of commission errors was lowest upon the activation of positive emotion. Moreover, under subliminal emotional priming, the rate of commission errors for the heroin abstainers group was significantly lower than that for the control group. However, regarding supraliminal emotional priming, there was no significant difference between the rates of commission errors for the two groups. These results suggest automatic positive emotional processing can enhance response inhibition among heroin abstainers. Moreover, under subliminal emotional priming, heroin abstainers had more enhanced response inhibition than the control groups. This study plays a significant role in understanding the effect of automatic emotional processing on response inhibition among drug addicts. This study also provides a theoretical basis for ensuring enhanced response inhibition among drug addicts.

Altered neural processing of social reward in male heroin abstainers.

Individuals who are addicted to drugs often face unfavourable social conditions and difficulty with social adaptation. Both may be closely related to impaired social cognitive ability. This study posits that social cognitive impairments likely arise from blunted social reward processing in drug users. This study aimed to explore the electrophysiological mechanism of social reward processing in people who abstain from using heroin (heroin abstainers). Twenty-eight male heroin abstainers and 27 matched controls completed the social incentive delay task. At the same time, their corresponding behaviour and electroencephalography data were recorded. The feedback-related negativity (FRN) elicited by positive outcomes was significantly more positive than that elicited by negative outcomes for the healthy controls. However, no significant difference in FRN was found between negative and positive outcomes among the heroin abstainers. There was no significant difference in P3 (a positive event-related potential component after FRN) elicited by negative and neutral outcomes in the heroin abstainers. Meanwhile, negative outcomes elicited greater P3 than neutral outcomes in the healthy controls. In addition, this study also found that withdrawal time was negatively correlated with the difference wave of FRN for the heroin abstainers. Heroin abstainers may be hyposensitive to the processing of social reward outcomes. In other words, they may have insufficient motivation to acquire social rewards. Abnormal social reward processing found in heroin abstainers can be improved with an increase in abstinence time. These results deepen our understanding of the social reward impairments associated with chronic drug use.

A novel convolutional neural network for kidney ultrasound images segmentation.

BACKGROUND AND OBJECTIVE: Ultrasound imaging has been widely used in the screening of kidney diseases. The localization and segmentation of the kidneys in ultrasound images are helpful for the clinical diagnosis of diseases. However, it is a challenging task to segment the kidney accurately from ultrasound images due to the interference of various factors. METHODS: In this paper, a novel multi-scale and deep-supervised CNN architecture is proposed to segment the kidney. The architecture consists of an encoder, a pyramid pooling module and a decoder. In the encoder, we design a multi-scale input pyramid with parallel branches to capture features at different scales. In the decoder, a multi-output supervision module is developed. The introduction of the multi-output supervision module enables the network to learn to predict more precise segmentation results scale-by-scale. In addition, we construct a kidney ultrasound dataset, which contains of 400 images and 400 labels. RESULTS: To highlight effectiveness of the proposed approach, we use six quantitative indicators to compare with several state-of-the-art methods on the same kidney ultrasound dataset. The results of our method on the six indicators of accuracy, dice, jaccard, precision, recall and ASSD are 98.86%, 95.86%, 92.18%, 96.38%, 95.47% and 0.3510, respectively. CONCLUSIONS: The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in kidney ultrasound image segmentation.

Theoretical Investigations for Kinetics of the Chemical Reactions: H + SiClx (x = 1, 2, 3).

Hydrogen atoms and SiClx (x = 1, 2, 3) radicals coexist during the hydrogenation of silicon tetrachloride (STC, SiCl4), an important process in the fabrication of industrial polysilicon. In this work, the mechanisms and kinetics of the reactions between H and SiClx (x = 1, 2, 3) were studied by theory. The structures and vibrational frequencies of reactants, products, intermediates, and transition states (TSs) were determined at the B2PLYP/may-cc-pVTZ level. The single-point energies of minima and saddle points were refined using the coupled-cluster single-double with triple perturbative (CCSD(T)) with the complete basis set extrapolation method. Some special treatments were designed to obtain reliable wave functions for unimolecular reactions without tight TSs by the density functional theory. Subsequently, Lennard-Jones (L-J) parameters between each intermediate (SiHClx) and bath gas (He) were obtained at the MP2/jul-cc-pVTZ level to derive reliable temperature- and pressure-dependent rate coefficients for unimolecular reactions according to the variational Rice-Ramsperger-Kassel-Marcus theory. For bimolecular reactions, rate coefficients were determined by the variational transition-state theory. The rate coefficients of barrierless reactions were derived based on the loose TSs with the maximum free energy. Finally, the master equation analysis was used to investigate the variation of the rate coefficients with pressure and temperature in the activated paths.

The Plastic Behavior in the Large Deflection Response of Fiber Metal Laminate Sandwich Beams under Transverse Loading.

The plastic behavior in the large deflection response of slender sandwich beams with fiber metal laminate (FML) face sheets and a metal foam core under transverse loading is studied. According to a modified rigid-perfectly plastic material approximation, an analytical model is developed, and simple formulae are obtained for the large deflection response of fully clamped FML sandwich beams, considering the interaction of bending and stretching. Finite element (FE) calculations are conducted, and analytical predictions capture numerical results reasonably in the plastic stage of large deflection. The influences of metal volume fraction, strength ratio of metal to composite layer, core strength, and punch size on the plastic behavior in the large deflection response of FML sandwich beams are discussed. It is suggested that, if the structural behavior of fiber-metal laminate sandwich beams is plasticity dominated, it is similar to that of metal sandwich beams. Moreover, both metal volume fraction and the strength ratio of metal to composite layer are found to be important for the plastic behavior in the large deflection response of fiber metal laminate sandwich beams, while core strength and punch size might have little influence on it.

MBANet: Multi-branch aware network for kidney ultrasound images segmentation.

Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware Network (MBANet), to segment kidney accurately and robustly. MBANet mainly consists of multi-scale feature pyramid (MSFP), multi-branch encoders (MBE) and master decoder. The design of MSFP can make the network more accessible to different kinds of class details at different scales. The information exchange between MBE can reduce the loss of feature information and improve the segmentation accuracy of the network. In addition, we designed a multi-scale fusion block (MFBlock) in the MBE to further extract and fuse more refined multi-scale image information. In order to further improve the robustness of MBANet, this paper also designed a step-by-step training mechanism. We validated the proposed approach and compared to several state-of-the-art approaches on the same kidney ultrasound datasets using six quantitative metrics. The results of our method on the six indicators of pixel accuracy (PA), intersection over union (IoU), precision, recall, specificity and F1-score (F1) are 98.83%, 92.38%, 97.10%, 95.03%, 99.46% and 0.9601, respectively. Compared with the comparison method, the average values on the six indicators are improved by about 2%. The evaluation results and segmentation results demonstrate that the proposed approach achieves the best overall performance on kidney ultrasound images segmentation.