BACKGROUND: Triangular fibrocartilage complex (TFCC) injuries, especially Palmer type IB, pose surgical management challenges due to associated distal radial ulnar joint (DRUJ) instability. Traditional surgeries entail risks of complications. Arthroscopic repair presents advantages but lacks consensus on optimal techniques. To evaluate arthroscopic dual-bone tunnel repair in patients with Palmer type IB TFCC injuries of the wrist.
METHODS: In this retrospective case series, grip strength ratio, joint range of motion, pain visual analogue scale (VAS), modified Mayo wrist score, and Disabilities of the Arm, Shoulder, and Hand (DASH) scores were assessed before and 12 months after surgery.
RESULTS: The cohort consisted of 45 patients. At 12 months, the grip strength ratio improved from 0.71 ± 0.08 to 0.93 ± 0.05 (P < 0.001), and wrist joint rotation increased from 126.78 ± 13.28° to 145.76 ± 8.52° (P < 0.001). VAS (1.60 ± 0.58 vs. 6.33 ± 0.91, P < 0.001), DASH (12.96 ± 3.18 vs. 46.87 ± 6.62, P < 0.001), and modified Mayo wrist (88.11 ± 4.43 vs. 63.78 ± 7.99, P < 0.001) scores all improved after surgery. The overall complication rate was 4.44%.
CONCLUSIONS: Arthroscopic dual-bone tunnel repair appears to be an effective intervention for alleviating wrist pain, restoring stability, and enhancing joint function in patients with TFCC Palmer type IB injuries.

OBJECTIVE: To explore the effects of different test positions on quantitative muscle strength of wrist and finger flexor muscle groups and to establish a standardized muscle strength test protocol for each muscle group.
METHODS: Forty healthy subjects (12 males and 28 females) were recruited. A portable digital quantitative muscle strength tester, Micro FET2TM, was used to measure the flexor muscle strength of each finger and the wrist joint at the 30° extension, 0° neutral, and 30° flexion, respectively. Palmar abduction strength of the thumb was measured at 30° and 60°, respectively. Ten subjects were randomly selected from the 40 subjects, and the quantitative muscle strength of each muscle group was tested again by the same operator after an interval of 10 to 15 days.
RESULTS: Except for the fact that in males, there was no significant difference in flexor muscle strength of thumb and wrist joint between 30° of wrist extension and neutral 0° position, the muscle strength of the other fingers flexion and wrist palmar flexor showed the following characteristics：30° of wrist extension > neutral 0° position > 30° of flexion, and the PAST was 30°>60°; The flexor muscle strength of all the subjects was thumb > index finger > middle finger > ring finger > little finger; All muscle strength values of male were greater than those of female, and the difference was statistically significant (P<0.05); There was no significant difference between the left and right side muscle strength values of all subjects (P>0.05). The reliability of muscle strength values measured at different times in 10 subjects was good.
CONCLUSIONS: The quantitative muscle strength of each muscle group of the hand and wrist is affected by the test position, and a standardized and uniformed test position should be adopted in the actual identification. Micro FET2TM has good reliability for hand and wrist quantitative muscle strength testing. The 30° extension of the wrist can be used as the best standardized test position for the flexion muscle strength of each finger and wrist joint. The 30° position can be used as the best standardized test position for PAST.
目的: 探究不同测试体位对手、腕屈曲肌群定量肌力的影响，建立各肌群的标准化肌力检测方案。方法: 征集40例健康受试者（男性12例，女性28例），使用Micro FET2TM便携式数字肌力测试仪，分别于腕关节背伸30°位、中立0°位和掌屈30°位测量各手指屈曲及腕关节掌屈肌力，分别于拇指掌侧外展30°位和60°位测量拇指掌侧外展肌力。从40例受试者中随机抽取10例，间隔10~15 d后由同一操作者再次测试各肌群定量肌力。结果: 除男性腕关节背伸30°位和中立0°位之间的拇指屈曲肌力、腕关节掌屈肌力差异无统计学意义（P>0.05），各指屈曲及腕掌屈肌力值主要呈现以下特点：腕关节背伸30°位>腕关节中立0°位>腕关节掌屈30°位，拇指掌侧外展30°位>60°位；所有受试者手指屈曲肌力均为拇指>示指>中指>环指>小指；男性各项肌力值均大于女性（P<0.05）；所有受试者左、右侧肌力值的差异均无统计学意义（P>0.05）。10例受试者不同时期测量肌力值的可靠性良好。结论: 手、腕各肌群定量肌力均受体位影响，鉴定过程中应采取规范、统一的测试体位。Micro FET2TM用于手、腕关节定量肌力测试具有良好的可靠性。腕关节背伸30°位可作为各手指屈曲肌力及腕关节掌屈肌力的最佳标准化检测条件。拇指掌侧外展30°位可作为拇指掌侧外展肌力的最佳标准化检测条件。.

OBJECTIVE: This study compared the biomechanical stability of transosseous repair and transosseous combined with capsular repair techniques to reattach the triangular fibrocartilage complex (TFCC) for distal radioulnar joint instability.
METHODS: Eight adult cadaveric upper-extremity specimens were studied. Each underwent peripheral ulnar-sided detachment of the deep and superficial TFCC fibers and repair. Four groups were prepared sequentially: intact TFCC, disrupted TFCC, transosseous repair, and combined transosseous with capsular repair. Forearm rotational torque was measured in three wrist positions: 60° flexion, neutral position, and 60° extension. Maximum dorsal and palmar ulnar translations in response to a 20-N traction load were measured at nine wrist positions after stabilizing the humerus and radius. Measurements were taken before and after TFCC disruption and following repair.
RESULTS: Clear instability of the radius relative to the ulna was observed after sectioning the deep and superficial fibers of the TFCC, and stability was markedly improved after reconstruction in all positions. Compared with the normal group, rotational torque was similar between the two repair methods. In the pronation palmar flexion and supination dorsal extension positions, dorsal-palmar translation was smaller in the combined transosseous with capsular repair group than in the transosseous repair-alone group.
CONCLUSIONS: Triangular fibrocartilage complex deep fibers are the primary stabilizing structure of the distal radioulnar joint. In this cadaveric study, the combined transosseous with capsular repair technique demonstrated less dorsal-palmar translation compared with the transosseous-alone repair technique.
CONCLUSIONS: Combined transosseous with capsular repair is expected to provide improved postoperative stability for patients with peripheral TFCC tears and distal radioulnar joint instability.

UNASSIGNED: Bone inflammation (osteitis) in early RA (ERA) manifests as bone marrow oedema (BME) and precedes the development of bone erosion. In this prospective, single-centre study, we developed an automated post-processing pipeline for quantifying the severity of wrist BME on T2-weighted fat-suppressed MRI.
UNASSIGNED: A total of 80 ERA patients [mean age 54 years (s.d. 12), 62 females] were enrolled at baseline and 49 (40 females) after 1 year of treatment. For automated bone segmentation, a framework based on a convolutional neural network (nnU-Net) was trained and validated (5-fold cross-validation) for 15 wrist bone areas at baseline in 60 ERA patients. For BME quantification, BME was identified by Gaussian mixture model clustering and thresholding. BME proportion (%) and relative BME intensity within each bone area were compared with visual semi-quantitative assessment of the RA MRI score (RAMRIS).
UNASSIGNED: For automated wrist bone area segmentation, overall bone Sørensen-Dice similarity coefficient was 0.91 (s.d. 0.02) compared with ground truth manual segmentation. High correlation (Pearson correlation coefficient r = 0.928, P < 0.001) between visual RAMRIS BME and automated BME proportion assessment was found. The automated BME proportion decreased after treatment, correlating highly (r = 0.852, P < 0.001) with reduction in the RAMRIS BME score.
UNASSIGNED: The automated model developed had an excellent segmentation performance and reliable quantification of both the proportion and relative intensity of wrist BME in ERA patients, providing a more objective and efficient alternative to RAMRIS BME scoring.

During activities of daily living (ADLs), the wrist is mainly engaged in positioning and directing the hand. Researches have demonstrated that restoring wrist mobility can significantly enhance the manipulation ability, reduce body distortion caused by motion compensation, and improve the quality of life for amputees. However, most daily activities, particularly the delicate ones, place high demands on the ability of wrist to maintain a certain rotation angle, also known as non-back-drivable ability, which poses a challenge to the design of prosthetic wrists. To address this issue, various solutions have been proposed, including motor holding brakes, high reduction ratio reducers, and worm gears. However, the motor holding brake only functions after a power outage and cannot continuously prevent torque from the load end. The latter two solutions may alter the transmission ratio, resulting in reduced movement speed and transmission efficiency. Therefore, how to design a miniaturized non-back-drivable mechanism without changing the transmission ratio so that the forearm rotational freedom can be locked at any position for any duration is a problem to be solved in the research of prosthetic wrist designs. This paper presents a line-contact based non-back-drivable clutch (NBDC) that does not cause changes in the transmission ratio, ensuring the motion performance of the prosthetic limb. At the same time, it does not introduce additional friction in the forward transmission process, guaranteeing the overall efficiency. Most importantly, it only allows the torque transmitting from the motor to the load, prevents the load reversely from driving back even in a power failure condition, significantly improving the stability, safety, and comfort. Detailed kinematic and static analyses of the working process has been conducted, and transient dynamics simulation has been performed to verify its effectiveness. Through experiments, it is demonstrated that the self-locking torque of the output end could reach approximately 600 Nmm, and the unlocking torque of the input end is about 80 Nmm, which can be effectively integrated in prosthetic wrist rotation joints, contributing to the performance, safety and energy saving of prosthetic joint systems.

The paper introduces professor WANG Haidong\'s clinical experience in treatment of wrist rheumatoid arthritis with acupotomy mobilization at the muscle regions (sinews/fascia) of three yang meridians of hand. Professor WANG Haidong believes that wrist rheumatoid arthritis belongs to the disorder of meridian muscle regions and is especially associated with the damage of the muscle regions of three yang meridians of hand running through the wrist. Under the guidance of meridian muscle region theory, on the basis of modern anatomy, and the treatment principle, \"needling the affected areas may treat disorders of sinews/fascia and dysfunction of meridians simultaneously\", acupotomy mobilization is adopted to balance sinews/fascia and bones, operated directly at the involved meridian muscle regions. Besides the foci (palpable knotted sites) on the distribution of muscle regions, acupoints along the affected meridians are stimulated in combination. With this therapy, after determining the location of illness, both the disorder of sinews/fascia and that of meridians can be treated.
介绍王海东教授运用针刀治疗类风湿腕关节炎的临床经验。王海东教授认为本病可归为经筋病的一种，以循行经过腕关节的手三阳经筋受损为发病关键。在经筋理论指导下，以现代解剖学为基础，依据“针至病所，筋脉同治”的治疗原则，采用针刀松解手三阳经筋治疗本病，以筋为先，调衡筋骨；在循筋取结筋病灶点的基础上配合循经取穴，筋、脉同治；明确病位，针至病所。.

BACKGROUND: Accurately estimating the 6D pose of snake-like wrist-type surgical instruments is challenging due to their complex kinematics and flexible design.
METHODS: We propose ERegPose, a comprehensive strategy for precise 6D pose estimation. The strategy consists of two components: ERegPoseNet, an original deep neural network model designed for explicit regression of the instrument\'s 6D pose, and an annotated in-house dataset of simulated surgical operations. To capture rotational features, we employ an Single Shot multibox Detector (SSD)-like detector to generate bounding boxes of the instrument tip.
RESULTS: ERegPoseNet achieves an error of 1.056 mm in 3D translation, 0.073 rad in 3D rotation, and an average distance (ADD) metric of 3.974 mm, indicating an overall spatial transformation error. The necessity of the SSD-like detector and L1 loss is validated through experiments.
CONCLUSIONS: ERegPose outperforms existing approaches, providing accurate 6D pose estimation for snake-like wrist-type surgical instruments. Its practical applications in various surgical tasks hold great promise.

Modular control of the muscle, which is called muscle synergy, simplifies control of the movement by the central nervous system. The purpose of this study was to explore the synergy in both the frequency and movement domains based on the non-negative Tucker decomposition (NTD) method. Surface electromyography (sEMG) data of 8 upper limb muscles in 10 healthy subjects under wrist flexion (WF) and wrist extension (WE) were recorded. NTD was selected for exploring the multi-domain muscle synergy from the sEMG data. The results showed two synergistic flexor pairs, Palmaris longus-Flexor Digitorum Superficialis (PL-FDS) and Extensor Carpi Radialis-Flexor Carpi Radialis (ECR-FCR), in the WF stage. Their spectral components are mainly in the respective bands 0-20 Hz and 25-50 Hz. And the spectral components of two extensor pairs, Extensor Digitorum-Extensor Carpi Ulnar (ED-ECU) and Extensor Carpi Radialis-Brachioradialis (ECR-B), are mainly in the respective bands 0-20 Hz and 7-45 Hz in the WE stage. Additionally, further analysis showed that the Biceps Brachii (BB) muscle was a shared muscle synergy module of the WE and WF stage, while the flexor muscles FCR, PL and FDS were the specific synergy modules of the WF stage, and the extensor muscles ED, ECU, ECR and B were the specific synergy modules of the WE stage. This study showed that NTD is a meaningful method to explore the multi-domain synergistic characteristics of multi-channel sEMG signals. The results can help us to better understand the frequency features of muscle synergy and shared and specific synergies, and expand the study perspective related to motor control in the nervous system.

Considerable progress has already been made in sweat sensors based on electrochemical methods to realize real-time monitoring of biomarkers. However, realizing long-term monitoring of multiple targets at the atomic level remains extremely challenging, in terms of designing stable solid contact (SC) interfaces and fully integrating multiple modules for large-scale applications of sweat sensors. Herein, a fully integrated wristwatch was designed using mass-manufactured sensor arrays based on hierarchical multilayer-pore cross-linked N-doped porous carbon coated by reduced graphene oxide (NPCs@rGO-950) microspheres with high hydrophobicity as core SC, and highly selective monitoring simultaneously for K+, Na+, and Ca2+ ions in human sweat was achieved, exhibiting near-Nernst responses almost without forming an interfacial water layer. Combined with computed tomography, solid-solid interface potential diffusion simulation results reveal extremely low interface diffusion potential and high interface capacitance (598 μF), ensuring the excellent potential stability, reversibility, repeatability, and selectivity of sensor arrays. The developed highly integrated-multiplexed wristwatch with multiple modules, including SC, sensor array, microfluidic chip, signal transduction, signal processing, and data visualization, achieved reliable real-time monitoring for K+, Na+, and Ca2+ ion concentrations in sweat. Ingenious material design, scalable sensor fabrication, and electrical integration of multimodule wearables lay the foundation for developing reliable sweat-sensing systems for health monitoring.

BACKGROUND: In the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution.
METHODS: We designed experiments involving three motor imagery tasks-wrist extension, wrist flexion, and wrist abduction-with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM).
RESULTS: After EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %.
UNASSIGNED: We compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.