A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications

Combined Medial and Lateral Approach Versus Paratricipital Approach in Open Reduction and Internal Fixation for Type C Distal Humerus Fracture: A Randomized Controlled Study

Objective Olecranon osteotomy and paratricipital approaches were widely used in the treatment of type C distal humerus fracture but some disadvantages exist, so a combined medial and lateral approach was designed. The objective of this study was to investigate and compare the clinical outcomes of combined medial and lateral approach with the paratricipital approach in open reduction and internal fixation of type C distal humerus fractures. Methods From May 2018 to April 2020, 37 patients with type C distal humerus fracture who accepted open reduction and internal fixation in our hospital were enrolled in this study. All cases were randomly divided into two groups according to the surgical approach: combined medial and lateral approach group (19 cases), paratricipital approach group (18 cases). All of the patients received open reduction and double vertical plates fixation. The operation and follow‐up indexes, including operation time, blood loss, incision length, triceps muscle strength, flexion‐extension arc of elbow and forearm rotation arc, were recorded and compared. Caja score was used to assess the quality of fractures reduction. Mayo Elbow Performance Score (MEPS) was used to evaluate the elbow function in the follow‐up. Complications such as incision infection, ulnar nerve injury, degenerative osteoarthritis, and heterotopic ossification were analyzed. Results The differences in age, gender, and AO classification of fractures between two groups were not statistically significant (p > 0.05). The sum of medial and lateral incision length of combined approach group was longer than the midline incision of paratricipital approach group (15.4 ± 0.8 vs. 14.6 ± 0.8, p  0.05), blood loss (71.3 ± 24.5 vs. 72.8 ± 24.6, p > 0.05), and Caja score (16.05 ± 5.67 vs. 15.56 ± 5.66, p > 0.05). During the follow‐up, the MEPS of combined approach group was higher than that of paratricipital approach group at 3 months postoperatively (80.5 ± 5.7 vs. 68.9 ± 8.1, p  0.05) and at the last follow‐up (86.8 ± 7.1 vs. 86.9 ± 7.7, p > 0.05) between the two groups. There was no significant difference in triceps muscle strength (p > 0.05), flexion‐extension arc (126.8 ± 5.3 vs. 128.9 ± 6.0, p > 0.05), and forearm rotation arc (163.2 ± 5.3 vs. 163.6 ± 4.8, p > 0.05) at the last follow‐up. Although the incidence of complication of combined approach group (15.8%) was lower than that of paratricipital approach group (22.2%), the difference was not statistically significant (p > 0.05). Conclusions The combined medial and lateral approach was an effective and safe way of open reduction and internal fixation for type C distal humerus fractures. Compared with the paratricipital approach, the combined medial and lateral approach could restore the elbow function more quickly postoperatively, and the long‐term results were comparable

Appropriate excision time of heterotopic ossification in elbow caused by trauma

Objective: The aim of this study was to investigate the optimal timing for the resection of heterotopic ossification (HO) of the elbow. Methods: We retrospectively reviewed 42 patients who were treated operatively for heterotopic ossification of the elbow from March 2010 to December 2014 at our institution. The patients were divided into early (before 12 months) and late (after 12 months) excision groups. In the early excision group (17 patients), the average time from the initial injury to HO excision was 7.4 (3–11) months, and in the late excision group (25 patients), the average time was 33.5 (12–240) months. Every patient was evaluated by range of motion (ROM), the Mayo Elbow Performance Score (MEPS), postoperative complications and HO recurrence. Results: The preoperative mean ROM in the late excision group was greater than that of the early excision group, suggesting that the ROM is expected to increase even without surgery. Both early and late surgery increased ROM and MEPS, but early surgery improved ROM and MEPS more than late surgery did (p < .05). Conclusions: Early excision of HO can provide better elbow function, as indicated by ROM and MEPS. Considering that there were no notable differences in postoperative ROM and MEPS, HO recurrence, or postoperative complications, we concluded that early excision is safe and that the time from an elbow injury to surgery may be shortened. Level of Evidence: Level III, therapeutic study. Keywords: Elbow, Heterotopic ossification, Trauma, Timing of excisio

Multi-Scenario Broadband Channel Measurement and Modeling for Sub-6 GHz RIS-Assisted Wireless Communication Systems

Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduct multi-scenario broadband channel measurements and modeling for RIS-assisted communications at the sub-6 GHz band. The measurements are carried out in three scenarios covering outdoor, indoor, and outdoor-to-indoor (O2I) environments, which suffer from non-line-of-sight (NLOS) propagation inherently. Three propagation modes including intelligent reflection with RIS, specular reflection with RIS and the mode without RIS, are taken into account in each scenario. In addition, considering the cascaded characteristics of RIS-assisted channel by nature, two modified empirical models including floating-intercept (FI) and close-in (CI) are proposed, which cover distance and angle domains. The measurement results rooted in 2096 channel acquisitions verify the prediction accuracy of these proposed models. Moreover, the propagation characteristics for RIS-assisted channels, including path loss (PL) gain, PL exponent, spatial consistency, time dispersion, frequency stationarity, etc., are compared and analyzed comprehensively. These channel measurement and modeling results may lay the groundwork for future applications of RIS-assisted communication systems in practice

A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems

Abstract The differentiation of pluripotent stem cells (PSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variabilities, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, PSC-to-cardiomyocyte (CM) differentiation is vulnerable to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), PSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial PSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor that can further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize PSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications