A Deep Learning-Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors.

The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hematoxylin and eosin-stained images. A deep learning-based system, Soft Tissue Tumor Box (STT-BOX), is presented herein, with only hematoxylin and eosin images for malignant STT identification from benign STTs with histopathologic similarity. STT-BOX assumed gastrointestinal stromal tumor as a baseline for malignant STT evaluation, and distinguished gastrointestinal stromal tumor from leiomyoma and schwannoma with 100% area under the curve in patients from three hospitals, which achieved higher accuracy than the interpretation of experienced pathologists. Particularly, this system performed well on six common types of malignant STTs from The Cancer Genome Atlas data set, accurately highlighting the malignant mass lesion. STT-BOX was able to distinguish ovarian malignant sex-cord stromal tumors without any fine-tuning. This study included mesenchymal tumors that originated from the digestive system, bone and soft tissues, and reproductive system, where the high accuracy of migration verification may reveal the morphologic similarity of the nine types of malignant tumors. Further evaluation in a pan-STT setting would be potential and prospective, obviating the overuse of immunohistochemistry and molecular tests, and providing a practical basis for clinical treatment selection in a timely manner.

RBF network-based adaptive sliding mode control strategy for the tendon-sheath driven joint of a prosthetic hand.

BACKGROUND: The complex in-hand manipulation puts forward higher requirements for the dexterity and joint control accuracy of the prosthetic hand. The tendon-sheath drive has important application potential in the fields of prosthetic hand to obtain higher dexterity. However, the existing control methods of tendon-sheath driven joint are mainly open-loop compensation based on friction model, which makes it difficult to achieve high-precision joint control. OBJECTIVE: The purpose of this work is to improve the position control accuracy of the tendon-sheath driven joint for the prosthetic hand. METHODS: The structure of the prosthetic hand is introduced, and the encoder and potentiometer are mounted on the driving motor and joint respectively. Then, the transfer function of the joint is established based on the dynamic model. The adaptive sliding mode control strategy based on RBF network is applied to realize the closed-loop feedback position control of the prosthetic hand joint. The stability of the system is demonstrated by Lyapunov theorem. RESULTS: Under the condition of constant and variable sheath curvature, the effectiveness of the controller is demonstrated by simulation and joint motion experiments, respectively. The results show that the closed-loop control has better position tracking ability than the open-loop control, and the designed controller can reduce the tracking error more obviously than the traditional algorithm. The high-precision position control can be realized by designing the controller based on the joint angle feedback. CONCLUSIONS: The research content has certain theoretical and practical significance for the development of joint high-precision control of tendon-sheath driven prosthetic hand. This is beneficial to the implementation of complex in-hand manipulation for prosthetic hand.

Basal Nanosuit of Graphite for High-Energy Hybrid Li Batteries.

Lithium (Li) metal anode has attracted tremendous attention for its highest capacity (3860 mAh g-1). Herein, we report that the formation of dead Li can be effectively suppressed through Li plating on porous lithiated graphite lamina (PLGL). A lithiophilic carbon layer was decorated on the lithiophobic basal plane of porous graphite lamina (PGL) with an industry-scalable slurry-coating strategy. Moreover, the higher delithiation potential of PLGL will ensure the complete stripping of the plated Li before its delithiation, thus dramatically enhancing the average Coulombic efficiency (ACE) of Li plating/stripping to 98.5% at a high Li plating/stripping capacity of 2 mAh cm-2 (∼1100 mAh g-1) at 2 mA cm-2. Even at an ultrahigh current density of 4 mA cm-2 (with Li capacity of 4 mAh cm-2 (∼1900 mAh g-1)), the ACE could still be maintained at 96.2% in an ordinary carbonate electrolyte.

A Simple Method for the Complete Performance Recovery of Degraded Ni-rich LiNi0.70Co0.15Mn0.15O2 Cathode via Surface Reconstruction.

The highly active surfaces of Ni-rich cathodes usually result in rapid surface degradation, which is manifested by poor cycle and rate capabilities. In this work, we propose a simple method to restore those degraded surfaces after storage. More importantly, the mechanism of surface degradation and recovery are investigated thoroughly. As storage in moist air, a lithium carbonate (Li2CO3) dominated impurity layer formed and tightly coated on the surface of the LiNi0.70Co0.15Mn0.15O2 particles. Except for the Li2CO3 layer, a NiO rock-salt structure was also found at near surface region by high-resolution transmission electron microscopy. These two inert species together impedance the transport of lithium ions and electrons, which result in no capacity at 4.3 V charge cutoff voltage of the stored material. We proposed a simple and effective method, i.e., three h calcination at 800 °C under oxygen flow. The restored LiNi0.70Co0.15Mn0.15O2 shows equivalent electrochemical performance compared to the pristine one. This is because the lithium ions in Li2CO3 layer return to the surface lattice of LiNi0.70Co0.15Mn0.15O2, and the NiO cubic phase transforms back to the layered structure with the oxidation of Ni2+. This method is not only insightful for cathode material design but also beneficial for practical application.

The correlation of PTPN4 expression with prognosis in breast cancer.

Precision medicine, applying knowledge of breast cancer's molecular subtypes, has improved the disease's prognosis. However, recurrence and chemoresistance are critical issues for breast cancer patients. PTPN4, a new potential therapeutic target, has not been studied sufficiently in breast cancer, and the potential role of PTPN4 in the prognosis of breast cancer patients is still unknown. In our study, data from 140 invasive breast cancer patients were retrospectively collected to identify the association between PTPN4 expression and clinical outcomes of these patients. The expressions of PTPN4 were detected by immunohistochemical analysis. Breast hyperplasia tissues showed higher expression of PTPN4. We found that PTPN4 expression was lower in breast cancer patients with relapse than in patients without relapse. Patients with an increased PTPN4 level had a significantly longer relapse-free survival and overall survival time. Decreased PTPN4 expression was an independent factor associated with relapse-free survival and overall survival, as shown by multivariate Cox regression analysis. The study found that PTPN4 is an attractive prognostic biomarker for predicting the clinical outcome and effective disease management of breast cancer patients.