[Management of transition growth hormone deficiency]. / è¿‡æ¸¡æœŸç”Ÿé•¿æ¿€ç´ ç¼ºä¹ç—‡çš„ç®¡ç†.

With an increasing understanding of growth hormone deficiency, there has been a growing emphasis on the management of transition growth hormone deficiency (TGHD) in clinical practice. The inadequate diagnosis and treatment of TGHD have been a major clinical concern, leading to the development of relevant guidelines and consensus internationally. This article summarizes the evaluation, diagnosis, treatment, and clinical challenges of TGHD based on these guidelines, consensus, and existing clinical studies, aiming to optimize and further improve the clinical diagnosis, treatment, and management of TGHD.

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches.

Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches over the past decade, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and algorithm feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. Overall, this review summarizes the current research status, significant findings, and challenges, aiming to inspire future research on predicting upper limb motion intentions based on sEMG.

[Reversal Effect of NVP-BEZ235 on Doxorubicin-Resistance in Burkitt Lymphoma RAJI Cell Line].

OBJECTIVE: To study the reversal effect of NVP-BEZ235 on doxorubicin resistance in Burkitt lymphoma RAJI cell line. METHODS: The doxorubicin-resistant cell line was induced by treating RAJI cells with a concentration gradient of doxorubicin. The levels of Pgp, p-AKT, and p-mTOR in cells were detected by Western blot. Cell viability was detected by MTT assay. IC50 was computed by SPSS. RESULTS: The doxorubicin-resistant Burkitt lymphoma cell line, RAJI/DOX, was established successfully. The expression of Pgp and the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line were both higher than those in RAJI cell line. NVP-BEZ235 downregulated the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line. NVP-BEZ235 inhibited the proliferation of RAJI/DOX cell line, and the effect was obvious when it was cooperated with doxorubicin. CONCLUSION: The constitutive activation of PI3K/AKT/mTOR pathway of RAJI/DOX cell line was more serious than RAJI cell line. NVP-BEZ235 reversed doxorubicin resistance of RAJI/DOX cell line by inhibiting the PI3K/AKT/mTOR signal pathway.

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer survival rates. Diverse datasets are required to improve the training and validation of deep learning (DL) systems for autonomous breast cancer diagnosis. However, only a small number of mammography datasets are publicly available. This constraint has created challenges when comparing different DL models using the same dataset. The primary contribution of this study is the comprehensive description of a selection of currently available public mammography datasets. The information available on publicly accessible datasets is summarized and their usability reviewed to enable more effective models to be developed for breast cancer detection and to improve understanding of existing models trained using these datasets. This study aims to bridge the existing knowledge gap by offering researchers and practitioners a valuable resource to develop and assess DL models in breast cancer diagnosis.

A female patient carrying a novel DMD mutation with non-random X-chromosome inactivation from a DMD family.

OBJECTIVE: To analyze the clinical phenotype and genetic characteristics of a female proband carrying a novel mutation in the DMD gene with non-random X-chromosome inactivation in a large pedigree with pseudohypertrophic muscular dystrophy. METHODS: Clinical information of the female proband, her monozygotic twin sister, and other family members were collected. Potential pathogenic variants were detected with Multiplex Ligation-dependent Probe Amplification (MLPA) and whole-exome sequencing (WES). Methylation-sensitive restriction enzyme (HhaI) was employed for X-chromosome inactivation analysis. RESULTS: The proband was a female over 5 years old, displayed clinical manifestations such as elevated creatine kinase (CK) levels and mild calf muscle hypertrophy. Her monozygotic twin sister exhibited normal CK levels and motor ability. Her uncle and cousin had a history of DMD. WES revealed that the proband carried a novel variant in the DMD (OMIM: 300,377) gene: NM_004006.3: c.3051_3053dup; NP_003997.2: p.Tyr1018*. In this pedigree, five out of six female members were carriers of this variant, while the cousin and uncle were hemizygous for this variant. X-chromosome inactivation analysis suggested non-random inactivation in the proband. CONCLUSION: The c.3051_3053dup (p.Tyr1018*) variant in the DMD gene is considered to be the pathogenic variant significantly associated with the clinical phenotype of the proband, her cousin, and her uncle within this family. Integrating genetic testing with clinical phenotype assessment can be a valuable tool for physicians in the diagnosis of progressive muscular dystrophies, such as Becker muscular dystrophy (BMD) and Duchenne muscular dystrophy (DMD).

Essential oil from Fructus Alpinia zerumbet ameliorates atherosclerosis by activating PPARγ-LXRα-ABCA1/G1 signaling pathway.

BACKGROUND: Atherosclerosis (AS) is a progressive chronic disease. Currently, cardiovascular diseases (CVDs) caused by AS is responsible for the global increased mortality. Yanshanjiang as miao herb in Guizhou of China is the dried and ripe fruit of Fructus Alpinia zerumbet. Accumulated evidences have confirmed that Yanshanjiang could ameliorate CVDs, including AS. Nevertheless, its effect and mechanism on AS are still largely unknown. PURPOSE: To investigate the role of essential oil from Fructus Alpinia zerumbet (EOFAZ) on AS, and the potential mechanism. METHODS: A high-fat diet (HFD) ApoE-/- mice model of AS and a oxLDL-induced model of macrophage-derived foam cells (MFCs) were reproduced to investigate the pharmacological properties of EOFAZ on AS in vivo and foam cell formation in vitro, respectively. The underlying mechanisms of EOFAZ were investigated using Network pharmacology and molecular docking. EOFAZ effect on PPARγ protein stability was measured using a cellular thermal shift assay (CETSA). Pharmacological agonists and inhibitors and gene interventions were employed for clarifying EOFAZ's potential mechanism. RESULTS: EOFAZ attenuated AS progression in HFD ApoE-/- mice. This attenuation was manifested by the reduced aortic intima plaque development, increased collagen content in aortic plaques, notable improvement in lipid profiles, and decreased levels of inflammatory factors. Moreover, EOFAZ inhibited the formation of MFCs by enhancing cholesterol efflux through activiting the PPARγ-LXRα-ABCA1/G1 pathway. Interestingly, the pharmacological knockdown of PPARγ impaired the beneficial effects of EOFAZ on MFCs. Additionally, our results indicated that EOFAZ reduced the ubiquitination degradation of PPARγ, and the chemical composition of EOFAZ directly bound to the PPARγ protein, thereby increasing its stability. Finally, PPARγ knockdown mitigated the protective effects of EOFAZ on AS in HFD ApoE-/- mice. CONCLUSION: These findings represent the first confirmation of EOFAZ's in vivo anti-atherosclerotic effects in ApoE-/- mice. Mechanistically, its chemical constituents can directly bind to PPARγ protein, enhancing its stability, while reducing PPARγ ubiquitination degradation, thereby inhibiting foam cell formation via activation of the PPARγ-LXRα-ABCA1/G1 pathway. Simultaneously, EOFAZ could ameliorates blood lipid metabolism and inflammatory microenvironment, thus synergistically exerting its anti-atherosclerotic effects.

1.2†W single-frequency Tm3+/Ho3+ co-doped fiber oscillator at 2050†nm.

In this Letter, a watt-level single-frequency fiber oscillator at 2050 nm was demonstrated for the first time, to the best of our knowlegde, in a linear laser cavity with a piece of an un-pumped Tm3+/Ho3+ co-doped fiber serving as a saturable absorber. With delicate optimization of mode filtering effect of the dynamic gratings formed in the saturable absorber, a maximum single-frequency laser output power of 1.2 W was achieved under a total bidirectional pump power of 5.8 W at 1570 nm, and the corresponding optical efficiency is 20.7%. This is, to the best of our knowledge, the highest power of a single-frequency fiber oscillator at the wavelength above 2 µm.

Widely tunable single-frequency Er-doped ZBLAN fiber laser with emission from 3.37 to 3.72†µm.

We demonstrate a widely tunable single-frequency Er-doped ZBLAN fiber laser operating on a 4F9/2→4I9/2 transition band. An uncoated germanium (Ge) plate serves as a narrow-bandwidth etalon and is inserted in the cavity to achieve a single longitudinal mode selection. Wavelength tuning from 3373.8 nm to 3718.5 nm was demonstrated by using a blazed diffraction grating at 3.5 µm. At the emission peak of 3465.6 nm, the laser yields over 100 mW single-frequency output power, with a 3 dB linewidth <6.9 MHz, and a slope efficiency (with respect to the incident 1990 nm pump power) of 20.3%. Such a tunable mid-infrared single-frequency fiber laser may serve as a versatile laser source in spectroscopy and sensing applications.

Efficient continuous-wave Nd:YVO4/KGW intracavity Raman laser.

We demonstrate an efficient Nd:YVO4/KGW intracavity Raman laser in continuous-wave (CW) scheme. With a V-shaped fundamental laser cavity and a short Stokes cavity in it, the oscillating beam sizes are designed to alleviate the thermal effect and to enhance the Raman gain for efficient CW operation. The output power of CW Stokes wave at 1177 nm reached 9.33 W under an incident laser diode pump power of 36.65 W, with corresponding optical efficiency being 25.5%. To the best of our knowledge, these are the highest Stokes output power and conversion efficiency of CW intracavity Raman lasers.

Pump quantum efficiency optimization of 3.5 µm Er-doped ZBLAN fiber laser for high-power operation.

976 nm + 1976 nm dual-wavelength pumped Er-doped ZBLAN fiber lasers are generally accepted as the preferred solution for achieving 3.5 µm lasing. However, the 2 µm band excited state absorption from the upper lasing level (4F9/2 → 4F7/2) depletes the Er ions population inversion, reducing the pump quantum efficiency and limiting the power scaling. In this work, we demonstrate that the pump quantum efficiency can be effectively improved by using a long-wavelength pump with lower excited state absorption rate. A 3.5 µm Er-doped ZBLAN fiber laser was built and its performances at different pump wavelengths were experimentally investigated in detail. A maximum output power at 3.46 µm of ~ 7.2 W with slope efficiency (with respect to absorbed 1990 nm pump power) of 41.2% was obtained with an optimized pump wavelength of 1990 nm, and the pump quantum efficiency was increased to 0.957 compared with the 0.819 for the conventional 1976 nm pumping scheme. Further power scaling was only limited by the available 1990 nm pump power. A numerical simulation was implemented to evaluate the cross section of excited state absorption via a theoretical fitting of experimental results. The potential of further power scaling was also discussed, based on the developed model.

Simultaneous Hip Implant Segmentation and Gruen Landmarks Detection.

The assessment of implant status and complications of Total Hip Replacement (THR) relies mainly on the clinical evaluation of the X-ray images to analyse the implant and the surrounding rigid structures. Current clinical practise depends on the manual identification of important landmarks to define the implant boundary and to analyse many features in arthroplasty X-ray images, which is time-consuming and could be prone to human error. Semantic segmentation based on the Convolutional Neural Network (CNN) has demonstrated successful results in many medical segmentation tasks. However, these networks cannot define explicit properties that lead to inaccurate segmentation, especially with the limited size of image datasets. Our work integrates clinical knowledge with CNN to segment the implant and detect important features simultaneously. This is instrumental in the diagnosis of complications of arthroplasty, particularly for loose implant and implant-closed bone fractures, where the location of the fracture in relation to the implant must be accurately determined. In this work, we define the points of interest using Gruen zones that represent the interface of the implant with the surrounding bone to build a Statistical Shape Model (SSM). We propose a multitask CNN that combines regression of pose and shape parameters constructed from the SSM and semantic segmentation of the implant. This integrated approach has improved the estimation of implant shape, from 74% to 80% dice score, making segmentation realistic and allowing automatic detection of Gruen zones. To train and evaluate our method, we generated a dataset of annotated hip arthroplasty X-ray images that will be made available.

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review.

Objective: Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly. Methods: A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review. Results: A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement." Conclusions: This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

Taurine alleviated hepatic steatosis in oleic acid-treated-HepG2 cells and rats fed a high-fat diet.

Taurine has been proven in many trials to alleviate the symptoms of metabolic associated fatty liver disease. Here its protective effect for hepatic steatosis and modulation of AMP-activated protein kinase and insulin signaling pathway were investigated. Steatotic HepG2 cell established with oleic acid (0.05 mmol/L), treated with taurine (5 mmol/L), dorsomorphin (10 µmol/L) for 24 h. Sprague Dawley rats were divided into regular and high-fat diet (HFD) groups, and their corresponding taurine (70 or 350 mg/kg BW/d) groups, fed for 8 weeks. In steatotic cell, taurine reduced the TG concentration and SREBP-1c, PPARγ, FAS, ACC, SCD1 protein levels, decreased phosphorylation of mTOR, IRS1 (Ser302), increased phosphorylation of AMPKα, LKB1, PI3K, Akt, ACC. While dorsomorphin eliminated taurine's TG-lowering effect. In HFD-fed rats, taurine reduced liver TG, serum TG, ALT, AST, IL-1ß, IL-4, TNF-α. The effects of taurine on the main factors of fatty acid synthesis were mostly consistent with cell experiments, and the reduction of microRNAs (451, 33, 291b) was aligned with the improvement in LKB1 and AMPK expression in HFD rats. Taurine alleviated steatosis-induced inhibition of IRS1-PI3K-Akt pathway, but suppressed its positively regulated downstream factor mTOR. In parallel, taurine reduced steatosis by activating LKB1-AMPKα pathway via phosphorylation and no-phosphorylation manner, then inhibiting SREBP-1c directly or by suppressing mTOR phosphorylation.

LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography.

Although deep learning (DL) techniques have been extensively researched in upper-limb myoelectric control, system robustness in cross-day applications is still very limited. This is largely caused by non-stable and time-varying properties of surface electromyography (sEMG) signals, resulting in domain shift impacts on DL models. To this end, a reconstruction-based method is proposed for domain shift quantification. Herein, a prevalent hybrid framework that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM), i.e. CNN-LSTM, is selected as the backbone. The paring of auto-encoder (AE) and LSTM, abbreviated as LSTM-AE, is proposed to reconstruct CNN features. Based on reconstruction errors (RErrors) of LSTM-AE, domain shift impacts on CNN-LSTM can be quantified. For a thorough investigation, experiments were conducted in both hand gesture classification and wrist kinematics regression, where sEMG data were both collected in multi-days. Experiment results illustrate that, when the estimation accuracy degrades substantially in between-day testing sets, RErrors increase accordingly and can be distinct from those obtained in within-day datasets. According to data analysis, CNN-LSTM classification/regression outcomes are strongly associated with LSTM-AE errors. The average Pearson correlation coefficients could reach -0.986 ± 0.014 and -0.992 ± 0.011, respectively.

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.

A systematic analysis of the global and regional burden of colon and rectum cancer and the difference between early- and late-onset CRC from 1990 to 2019.

The burden of colorectal cancer (CRC) varies substantially across different geographical locations. However, there was no further quantitative analysis of regional social development and the disease burden of CRC. In addition, the incidence of early- and late-onset CRC has increased rapidly in developed and developing regions. The main purpose of this study was to investigate the trends in CRC burden across different regions, in addition to the epidemiological differences between early and late-onset CRC and their risk factors. In this study, estimated annual percentage change (EAPC) was employed to quantify trends in age-standardized incidence rate (ASIR), mortality rate, and disability-adjusted life-years. Restricted cubic spline models were fitted to quantitatively analyze the relationship between trends in ASIR and Human Development Index (HDI). In addition, the epidemiological characteristics of early- and late-onset CRC were investigated using analyses stratified by age groups and regions. Specifically, meat consumption and antibiotic use were included to explore the differences in the risk factors for early- and late-onset CRC. The quantitative analysis showed that the ASIR of CRC was exponentially and positively correlated with the 2019 HDI in different regions. In addition, the growing trend of ASIR in recent years varied substantially across HDI regions. Specifically, the ASIR of CRC showed a significant increase in developing countries, while it remained stable or decreased in developed countries. Moreover, a linear correlation was found between the ASIR of CRC and meat consumption in different regions, especially in developing countries. Furthermore, a similar correlation was found between the ASIR and antibiotic use in all age groups, with different correlation coefficients for early-onset and late-onset CRC. It is worth mentioning that the early onset of CRC could be attributable to the unrestrained use of antibiotics among young people in developed countries. In summary, for better prevention and control of CRC, governments should pay attention to advocate self-testing and hospital visits among all age groups, especially among young people at high risk of CRC, and strictly control meat consumption and the usage of antibiotics.

Actuation system modelling and design optimization for an assistive exoskeleton for disabled and elderly with series and parallel elasticity.

BACKGROUND: The aim of a robotic exoskeleton is to match the torque and angular profile of a healthy human subject in performing activities of daily living. Power and mass are the main requirements considered in the robotic exoskeletons that need to be reduced so that portable designs to perform independent activities by the elderly users could be adopted. OBJECTIVE: This paper evaluates a systematic approach for the design optimization strategies of elastic elements and implements an actuator design solution for an ideal combination of components of an elastic actuation system while providing the same level of support to the elderly. METHODS: A multi-factor optimization technique was used to determine the optimum stiffness and engagement angle of the spring within its elastic limits at the hip, knee and ankle joints. An actuator design framework was developed for the elderly users to match the torque-angle characteristics of the healthy human with the best motor and transmission system combined with series or parallel elasticity in an elastic actuator. RESULTS: With the optimized spring stiffness, a parallel elastic element significantly reduced the torque and power requirements up to 90% for some manoeuvres for the users to perform ADL. When compared with the rigid actuation system, the optimized robotic exoskeleton actuation system reduced the power consumption of up to 52% using elastic elements. CONCLUSION: A lightweight, smaller design of an elastic actuation system consuming less power as compared to a rigid system was realized using this approach. This will help to reduce the battery size and hence the portability of the system could be better adopted to support elderly users in performing daily living activities. It was established that parallel elastic actuators (PEA) can reduce the torque and power better than series elastic actuators (SEA) in performing everyday tasks for the elderly.

High-power, high-efficiency single-frequency DBR fiber laser at 1064†nm based on Yb3+-doped silica fiber.

A high-power, high-efficiency single-frequency fiber laser at 1064 nm was demonstrated based on a distributed Bragg reflector (DBR) all-silica-fiber configuration. A single-frequency laser with an output power of 642 mW and slope efficiency of 66.4% with respect to absorbed pump power was achieved from a 1.2-cm-long commercially available Yb3+-doped silica fiber. To the best of our knowledge, this is the highest single-frequency laser power and efficiency obtained from the DBR all-silica fiber laser. The work presented here paves the way for the development of high-power, robust, and cost-effective single-frequency Yb3+-doped all-silica fiber lasers.