A Randomized Crossover Trial of Mixed Meal Tolerance Test Response in People with Type 1 Diabetes on Insulin Pump Therapy and YG1699 or Dapagliflozin.

YG1699 is a novel inhibitor of sodium-glucose cotransporter 1 (SGLT1) and SGLT2. This double-blind, 3-way crossover trial compared YG1699 to dapagliflozin as an adjunct to insulin in people with type 1 diabetes (T1D) on insulin pump therapy. Treatment periods included four mixed meal tolerance tests (MMTTs) and insulin withdrawal tests per person. Nineteen adults with T1D were randomized to YG1699 10 mg, YG1699 25 mg, and dapagliflozin 10 mg once daily for 1 week in different orders. The primary end point was the difference in area under the curve (AUC) in plasma glucose (AUC0-120min) after an MMTT between treatment groups. Mean change in plasma glucose after an MMTT (AUC0-120min) was lower for YG1699 10 mg vs. dapagliflozin (89.51% of baseline vs. 102.13%, 90% confidence interval (CI) vs. dapagliflozin, -6% to -16%, P = 0.0003) and for YG1699 25 mg (84.83% vs. 102.13%, 90% CI vs. dapagliflozin -13% to -22%, P < 0.0001). At 120 minutes, mean glucose values on no treatment, dapagliflozin, YG1699 10 mg, and YG1699 25 mg were 149 (SE 7.6), 141 (SE 6.1), 128 (SE 6.9), and 115 (SE 7.8) mg/dL, respectively. Insulin dose requirements were lower for YG1699 10 mg and 25 mg vs. dapagliflozin for bolus insulin, and for YG1699 10 mg vs. dapagliflozin for total daily insulin. Safety profiles were similar between treatment groups. YG1699 reduced post-prandial glucose more than dapagliflozin in people with T1D on insulin pump therapy. The results were consistent with dual SGLT1/SGLT2 inhibition by YG1699.

The incremental value of Mycobacterium tuberculosis trace nucleic acid detection in CT-guided percutaneous biopsy needle rinse solutions for the diagnosis of tuberculosis.

Introduction: Tuberculosis (TB) diagnosis still faces challenges with high proportion of bacteriologic test negative incidences worldwide. We assessed the diagnostic value of digital PCR (dPCR) analysis of ultramicro Mycobacterium tuberculosis (M.tb) nucleic acid in CT-guided percutaneous biopsy needle rinse solution (BNRS) for TB. Methods: BNRS specimens were consecutively collected and total DNA was purified. The concentrations of M.tb-specific IS6110 and IS1081 were quantified using droplet dPCR. The diagnostic performances of BNRS-dPCR and its sensitivity in comparison with conventional tests were analyzed. Results: A total of 106 patients were enrolled, 63 of whom were TB (48 definite and 15 clinically suspected TB) and 43 were non-TB. The sensitivity of BNRS IS6110 OR IS1081-dPCR for total, confirmed and clinically suspected TB was 66.7%, 68.8% and 60.0%, respectively, with a specificity of 97.7%. Its sensitivity was higher than that of conventional etiological tests, including smear microscopy, mycobacterial culture and Xpert using sputum and BALF samples. The positive detection rate in TB patients increased from 39.3% for biopsy AFB test alone to 73.2% when combined with BNRS-dPCR, and from 71.4% for biopsy M.tb molecular detection alone to 85.7% when combined with BNRS-dPCR. Conclusion: Our results preliminarily indicated that BNRS IS6110 OR IS1081-dPCR is a feasible etiological test, which has the potential to be used as a supplementary method to augment the diagnostic yield of biopsy and improve TB diagnosis.

Easily applicable predictive score for MPR based on parameters before neoadjuvant chemoimmunotherapy in operable NSCLC: a single-center, ambispective, observational study.

BACKGROUND: Neoadjuvant chemoimmunotherapy (NACI) is promising for resectable nonsmall cell lung cancer (NSCLC), but predictive biomarkers are still lacking. The authors aimed to develop a model based on pretreatment parameters to predict major pathological response (MPR) for such an approach. METHODS: The authors enrolled operable NSCLC treated with NACI between March 2020 and May 2023 and then collected baseline clinical-pathology data and routine laboratory examinations before treatment. The efficacy and safety data of this cohort was reported and variables were screened by Logistic and Lasso regression and nomogram was developed. In addition, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to assess its power. Finally, internal cross-validation and external validation was performed to assess the power of the model. RESULTS: In total, 206 eligible patients were recruited in this study and 53.4% (110/206) patients achieved MPR. Using multivariate analysis, the predictive model was constructed by seven variables, prothrombin time (PT), neutrophil percentage (NEUT%), large platelet ratio (P-LCR), eosinophil percentage (EOS%), smoking, pathological type, and programmed death ligand-1 (PD-L1) expression finally. The model had good discrimination, with area under the receiver operating characteristic curve (AUC) of 0.775, 0.746, and 0.835 for all datasets, cross-validation, and external validation, respectively. The calibration curves showed good consistency, and decision curve analysis indicated its potential value in clinical practice. CONCLUSION: This real world study revealed favorable efficacy in operable NSCLC treated with NACI. The proposed model based on multiple clinically accessible parameters could effectively predict MPR probability and could be a powerful tool in personalized medication.

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.

BACKGROUND: Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning. MATERIALS AND METHODS: This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively. RESULTS: A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit. CONCLUSIONS: The radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases.

Background: Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. Methods: This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. Results: There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. Conclusions: The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.

Ultrasensitive and robust mechanoluminescent living composites.

Mechanosensing, the transduction of extracellular mechanical stimuli into intracellular biochemical signals, is a fundamental property of living cells. However, endowing synthetic materials with mechanosensing capabilities comparable to biological levels is challenging. Here, we developed ultrasensitive and robust mechanoluminescent living composites using hydrogels embedded with dinoflagellates, unicellular microalgae with a near-instantaneous and ultrasensitive bioluminescent response to mechanical stress. Not only did embedded dinoflagellates retain their intrinsic mechanoluminescence, but with hydrophobic coatings, living composites had a lifetime of ~5 months under harsh conditions with minimal maintenance. We 3D-printed living composites into large-scale mechanoluminescent structures with high spatial resolution, and we also enhanced their mechanical properties with double-network hydrogels. We propose a counterpart mathematical model that captured experimental mechanoluminescent observations to predict mechanoluminescence based on deformation and applied stress. We also demonstrated the use of the mechanosensing composites for biomimetic soft actuators that emitted colored light upon magnetic actuation. These mechanosensing composites have substantial potential in biohybrid sensors and robotics.

Electrowetting of a Soft Elastic Gel.

A liquid drop may change its wettability on a surface with an applied voltage, known as electrowetting. Herein, we report an electrowetting phenomenon of a soft elastic gel, where gel elasticity plays an important role. We have designed experiments to measure the voltage-dependent adhesion energy between the gel and a metal electrode and proposed an electromechanical model for the electrowetting behavior of the gel. Our experiments have revealed that the voltage-dependent adhesion energy is an intrinsic material property of the polyvinyl chloride (PVC) gel, not affected by the electrode size, geometry, and the stressed state of the gel. Finally, we demonstrate that the predeformation of the gel can be used to tailor its electrowetting behavior.

Genetic association of telomere length, obesity and tobacoo smoking with idiopathic pulmonary fibrosis risk.

BACKGROUND: Due to the inadequacy of published evidence, association of telomere length (TL), obesity and tobacco smoking with idiopathic pulmonary fibrosis (IPF) remains unclear. The aim of the study was to explore whether these exposures genetically affected the risk of the disease. METHODS: Genetic variants from genome-wide association studies for TL, body mass index (BMI), body fat percentage (BFP) and tobacco smoking (including maternal smoking) were used as instrumental variables. Inverse-variance weighted were mainly adopted to determine the genetic association of these exposures with IPF. All analyses were conducted by R-software (version 3.6.1). RESULTS: Firstly, longer TL was associated with the decreased risk of IPF (OR = 0.475 per SD increase in TL, 95%CI = 0.336 ~ 0.670, P<0.001). Secondly, higher levels of BMI and BFP were related to the increased risk of the disease (OR = 1.425 per SD increase in BMI level, 95%CI = 1.114 ~ 1.823, P = 0.005; OR = 1.702 per SD increase in BFP level, 95%CI = 1.202 ~ 2.409, P = 0.003). Thirdly, maternal smoking was implicated in the increased risk of the disease (OR = 13.183 per SD increase in the prevalence of maternal smoking, 95%CI = 1.820 ~ 95.484, P = 0.011). CONCLUSION: TL should be a genetic risk factor for IPF. Obesity and exposure to tobacco smoking as a fetus might also contribute to the development of this fibrotic diseases. These findings should be verified by future studies.

Local response and emerging nonlinear elastic length scale in biopolymer matrices.

Nonlinear stiffening is a ubiquitous property of major types of biopolymers that make up the extracellular matrices (ECM) including collagen, fibrin, and basement membrane. Within the ECM, many types of cells such as fibroblasts and cancer cells have a spindle-like shape that acts like two equal and opposite force monopoles, which anisotropically stretch their surroundings and locally stiffen the matrix. Here, we first use optical tweezers to study the nonlinear force-displacement response to localized monopole forces. We then propose an effective-probe scaling argument that a local point force application can induce a stiffened region in the matrix, which can be characterized by a nonlinear length scale R* that increases with the increasing force magnitude; the local nonlinear force-displacement response is a result of the nonlinear growth of this effective probe that linearly deforms an increasing portion of the surrounding matrix. Furthermore, we show that this emerging nonlinear length scale R* can be observed around living cells and can be perturbed by varying matrix concentration or inhibiting cell contractility.

Banking of perinatal mesenchymal stem/stromal cells for stem cell-based personalized medicine over lifetime: Matters arising.

Mesenchymal stromal/stem cells (MSCs) are currently applied in regenerative medicine and tissue engineering. Numerous clinical studies have indicated that MSCs from different tissue sources can provide therapeutic benefits for patients. MSCs derived from either human adult or perinatal tissues have their own unique advantages in their medical practices. Usually, clinical studies are conducted by using of cultured MSCs after thawing or short-term cryopreserved-then-thawed MSCs prior to administration for the treatment of a wide range of diseases and medical disorders. Currently, cryogenically banking perinatal MSCs for potential personalized medicine for later use in lifetime has raised growing interest in China as well as in many other countries. Meanwhile, this has led to questions regarding the availability, stability, consistency, multipotency, and therapeutic efficiency of the potential perinatal MSC-derived therapeutic products after long-term cryostorage. This opinion review does not minimize any therapeutic benefit of perinatal MSCs in many diseases after short-term cryopreservation. This article mainly describes what is known about banking perinatal MSCs in China and, importantly, it is to recognize the limitation and uncertainty of the perinatal MSCs stored in cryobanks for stem cell medical treatments in whole life. This article also provides several recommendations for banking of perinatal MSCs for potentially future personalized medicine, albeit it is impossible to anticipate whether the donor will benefit from banked MSCs during her/his lifetime.

Radiomics analysis of lung CT for multidrug resistance prediction in active tuberculosis: a multicentre study.

OBJECTIVES: Multidrug-resistant TB (MDR-TB) is a severe burden and public health threat worldwide. This study aimed to develop a radiomics model based on the tree-in-bud (TIB) sign and nodules and validate its predictive performance for MDR-TB. METHODS: We retrospectively recruited 454 patients with proven active TB from two hospitals and classified them into three training and testing cohorts: TIB (n = 295, 102), nodules (n = 302, 97), and their combination (n = 261, 81). Radiomics features relating to TIB and nodules were separately extracted. The maximal information coefficient and recursive feature elimination were used to select informative features per the two signs. Two radiomics models were constructed to predict MDR-TB using a random forest classifier. Then, a combined model was built incorporating radiomics features based on these two signs. The capability of the models in the combined training and testing cohorts was validated with ROC curves. RESULTS: Sixteen features were extracted from TIB and 15 from nodules. The AUCs of the combined model were slightly higher than those of the TIB model in the combined training cohort (0.911 versus 0.877, p > 0.05) and testing cohort (0.820 versus 0.786, p < 0.05) and similar to the performance of the nodules model in the combined training cohort (0.911 versus 0.933, p > 0.05) and testing cohort (0.820 versus 0.855, p > 0.05). CONCLUSIONS: The CT-based radiomics models hold promise for use as a non-invasive tool in the prediction of MDR-TB. CLINICAL RELEVANCE STATEMENT: Our study revealed that complementary information regarding MDR-TB can be provided by radiomics based on the TIB sign and nodules. The proposed radiomics models may be new markers to predict MDR in active TB patients. KEY POINTS: • This is the first study to build, validate, and apply radiomics based on tree-in-bud sign and nodules for the prediction of MDR-TB. • The radiomics model showed a favorable performance for the identification of MDR-TB. • The combined model holds potential to be used as a diagnostic tool in routine clinical practice.

Liquid Crystal Elastomer Based Dexterous Artificial Motor Unit.

Despite the great advancement in designing diverse soft robots, they are not yet as dexterous as animals in many aspects. One challenge is that they still lack the compact design of an artificial motor unit with a great comprehensive performance that can be conveniently fabricated, although many recently developed artificial muscles have shown excellent properties in one or two aspects. Herein, an artificial motor unit is developed based on gold-coated ultrathin liquid crystal elastomer (LCE) film. Subject to a voltage, Joule heating generated by the gold film increases the temperature of the LCE film underneath and causes it to contract. Due to the small thermal inertial and electrically controlling method of the ultrathin LCE structure, its cyclic actuation speed is fast and controllable. It is shown that under electrical stimulation, the actuation strain of the LCE-based motor unit reaches 45%, the strain rate reaches 750%/s, and the output power density is as high as 1360 W kg-1 . It is further demonstrated that the LCE-based motor unit behaves like an actuator, a brake, or a nonlinear spring on demand, analogous to most animal muscles. Finally, as a proof-of-concept, multiple highly dexterous artificial neuromuscular systems are demonstrated using the LCE-based motor unit.

A wearable cardiac ultrasound imager.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Research on efficient feature extraction: Improving YOLOv5 backbone for facial expression detection in live streaming scenes.

Facial expressions, whether simple or complex, convey pheromones that can affect others. Plentiful sensory input delivered by marketing anchors' facial expressions to audiences can stimulate consumers' identification and influence decision-making, especially in live streaming media marketing. This paper proposes an efficient feature extraction network based on the YOLOv5 model for detecting anchors' facial expressions. First, a two-step cascade classifier and recycler is established to filter invalid video frames to generate a facial expression dataset of anchors. Second, GhostNet and coordinate attention are fused in YOLOv5 to eliminate latency and improve accuracy. YOLOv5 modified with the proposed efficient feature extraction structure outperforms the original YOLOv5 on our self-built dataset in both speed and accuracy.

Highly robust and soft biohybrid mechanoluminescence for optical signaling and illumination.

Biohybrid is a newly emerging and promising approach to construct soft robotics and soft machines with novel functions, high energy efficiency, great adaptivity and intelligence. Despite many unique advantages of biohybrid systems, it is well known that most biohybrid systems have a relatively short lifetime, require complex fabrication process, and only remain functional with careful maintenance. Herein, we introduce a simple method to create a highly robust and power-free soft biohybrid mechanoluminescence, by encapsulating dinoflagellates, bioluminescent unicellular marine algae, into soft elastomeric chambers. The dinoflagellates retain their intrinsic bioluminescence, which is a near-instantaneous light response to mechanical forces. We demonstrate the robustness of various geometries of biohybrid mechanoluminescent devices, as well as potential applications such as visualizing external mechanical perturbations, deformation-induced illumination, and optical signaling in a dark environment. Our biohybrid mechanoluminescent devices are ultra-sensitive with fast response time and can maintain their light emission capability for weeks without special maintenance.

Mesenchymal Stem/Stromal Cells in Progressive Fibrogenic Involvement and Anti-Fibrosis Therapeutic Properties.

Fibrosis refers to the connective tissue deposition and stiffness usually as a result of injury. Fibrosis tissue-resident mesenchymal cells, including fibroblasts, myofibroblast, smooth muscle cells, and mesenchymal stem/stromal cells (MSCs), are major players in fibrogenic processes under certain contexts. Acknowledging differentiation potential of MSCs to the aforementioned other types of mesenchymal cell lineages is essential for better understanding of MSCs' substantial contributions to progressive fibrogenesis. MSCs may represent a potential therapeutic option for fibrosis resolution owing to their unique pleiotropic functions and therapeutic properties. Currently, clinical trial efforts using MSCs and MSC-based products are underway but clinical data collected by the early phase trials are insufficient to offer better support for the MSC-based anti-fibrotic therapies. Given that MSCs are involved in the coagulation through releasing tissue factor, MSCs can retain procoagulant activity to be associated with fibrogenic disease development. Therefore, MSCs' functional benefits in translational applications need to be carefully balanced with their potential risks.

Strengthening regulations, recent advances and remaining barriers in stem cell clinical translation in China: 2015-2021 in review.

A new regulatory regime is being implemented under strict scrutiny for translation of stem cell medical practices since 2015 in China. The new mode of governance is strengthening to curb the marketing of unproven stem cell therapeutic products. This article begins with a brief historical overview of stem cell research and development and then focuses on the policies and country-level guidelines in the past years for stem cell translational research. This study reveals several key observations on the major progress made and the challenges associated with clinical translation of stem cells in China. Given that stem cells or stem cell-based therapeutic products are already considered as biological 'drugs', this study would be conducive to a better understanding of China's approach to stem cell translational research, marketisation and industrialization in progress.

Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids.

Objectives: To develop and apply magnetic resonance imaging (MRI) parameter-based machine learning (ML) models to predict non-perfused volume (NPV) reduction and residual regrowth of uterine fibroids after high-intensity focused ultrasound (HIFU) ablation.Methods: MRI data of 573 uterine fibroids in 410 women who underwent HIFU ablation from the Chongqing Haifu Hospital (training set, N = 405) and the First Affiliated Hospital of Chongqing Medical University (testing set, N = 168) were retrospectively analyzed. Fourteen MRI parameters were screened for important predictors using the Boruta algorithm. Multiple ML models were constructed to predict NPV reduction and residual fibroid regrowth in a median of 203.0 (interquartile range: 122.5-367.5) days. Furthermore, optimal models were used to plot prognostic prediction curves.Results: Fourteen features, including postoperative NPV, indicated predictive ability for NPV reduction. Based on the 10-fold cross-validation, the best average performance of multilayer perceptron achieved with R2 was 0.907. In the testing set, the best model was linear regression (R2 =0.851). Ten features, including the maximum thickness of residual fibroids, revealed predictive power for residual fibroid regrowth. Random forest model achieved the best performance with an average area under the curve (AUC) of 0.904 (95% confidence interval (CI), 0.869-0.939), which was maintained in the testing set with an AUC of 0.891 (95% CI, 0.850-0.929).Conclusions: ML models based on MRI parameters can be used for prognostic prediction of uterine fibroids after HIFU ablation. They can potentially serve as a new method for learning more about ablated fibroids.

Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU.

To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.

Quantitative evaluation of the microjet velocity and cavitation erosion on a copper plate produced by a spherical cavity focused transducer at the high hydrostatic pressure.

Cavitation erosion at the high hydrostatic pressure causes the equipment to operate abnormally for the huge economic losses. Few methods can quantitatively evaluate the cavitation erosion intensity. In order to solve this problem, the cavitation erosion on a copper plate was carried out in a spherical cavity focused transducer system at the hydrostatic pressure of 3, 6, and 10 MPa. Meanwhile, the corresponding cavitation threshold, the initial bubble radius, and the microjet velocity in the ultrasonic field are theoretically analyzed to determine the dimension and velocity of microjet based on the following hypotheses: (1) the influence of the coalescence on the bubble collapse is ignored; (2) the dimension of the microjet is equal to the largest bubble size without the influence of gravity and buoyancy. Using the Westervelt equation for the nonlinear wave propagation and the Johnson-Cook material constitutive model for the high strain rate, a microjet impact model of the multi-bubble cavitation was constructed. In addition, through the analogy with the indentation test, an inversion model was proposed to calculate the microjet velocity and the cavitation erosion intensity. The microjet geometric model was constructed from the dimension and velocity of the microjet. The continuous microjet impact was proposed according to the equivalent impact momentum and solved by the finite element method. The relative errors of the pit depth are 4.02%, 3.34%, and 1.84% at the hydrostatic pressure of 3, 6, and 10 MPa, respectively, and the relative error in the evolution of pit morphology is 7.33% at 10 MPa, which verified the reliability of the proposed models. Experimental and simulation results show that the higher the hydrostatic pressure, the greater the pit depth, pit diameter, the pit-to-microjet diameter ratio, and the cavitation erosion intensity, but the smaller the pit diameter-to-depth ratio. The cavitation erosion intensity becomes significant with the ongoing ultrasonic exposure. In addition, a comparison of the cavitation pit morphology in the microjet pulsed and continuous impact modes shows that the continuous impact mode is effective without the elastic deformation caused by the residual stress. Using the cavitation pit morphology at the different hydrostatic pressures, the microjet velocity can be estimated successfully and accurately in a certain range, whose corresponding errors at the lower and upper limit are 5.98% and 0.11% at 3 MPa, 6.62% and 9.14% at 6 MPa, 6.54% and 5.42% at 10 MPa, respectively. Our proposed models are valid only when the cavitation pit diameter-to-depth ratio is close to 1. Altogether, the cavitation erosion induced by multi-bubble collapses in the focal region of a focused transducer could be evaluated both experimentally and numerically. Using the cavitation pit morphology and the inversion model, the microjet velocity in a certain range could be estimated successfully with satisfactory accuracy.