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This report presents the case of an ice hockey player diagnosed with a tear in the anterior horn of the lateral meniscus at 19 years of age. In addition, we suggest a rehabilitation exercise program that enabled an early return to play for an ice hockey athlete in a university sports league. The patient underwent arthroscopic meniscal repair and was discharged one day after surgery. A three-stage rehabilitation program was developed, as follows: Phase I, the protective phase (reduce inflammation symptoms and restore range of motion to enable weight-bearing); Phase II, the restorative phase (improve the strength of the quadriceps muscle and recover cardiovascular endurance); Phase III, the return to full functional activity (familiarize the patient with sport-specific skills and recover a symmetric functional level on both sides). After completing the 10-week rehabilitation program, the patient returned to play and took part in games 71 and 84 days after surgery. The patient had no meniscus reinjury in the 10 years after surgery while playing in university and professional ice hockey leagues. The functional performance-focused rehabilitation program presented in this case report resulted in an early return to sport and good long-term outcomes. [ABSTRACT FROM AUTHOR]

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze’s overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application. [ABSTRACT FROM AUTHOR]

Objectives: Exposure to humidifier disinfectants has been linked to respiratory diseases, including interstitial lung disease, asthma, and pneumonia. Consequently, numerous toxicological studies have explored respiratory damage as both a necessary and sufficient condition for these diseases. We systematically reviewed and integrated evidence from toxicological studies by applying the evidence integration method established in previous research to confirm the biological plausibility of the association between exposure and disease., Methods: We conducted a literature search focusing on polyhexamethylene guanidine phosphate (PHMG) and chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT), the primary ingredients in humidifier disinfectants. We selected relevant studies based on their quality and the population, exposure, comparator, outcome (PECO) statements. These studies were categorized into 3 lines of evidence: hazard information, animal studies, and mechanistic studies. Based on a systematic review, we integrated the evidence to develop an aggregate exposure pathway-adverse outcome pathway (AEP-AOP) model for respiratory damage. The reliability and relevance of our findings were assessed by comparing them with the hypothesized pathogenic mechanisms of respiratory diseases., Results: The integration of each AEP-AOP component for PHMG and CMIT/MIT led to the development of an AEP-AOP model, wherein disinfectants released from humidifiers in aerosol or gaseous form reached target sites, causing respiratory damage through molecular initiating events and key events. The model demonstrated high reliability and relevance to the pathogenesis of respiratory diseases., Conclusion: The AEP-AOP model developed in this study provides strong evidence that exposure to humidifier disinfectants causes respiratory diseases. This model demonstrates the pathways leading to respiratory damage, a hallmark of these conditions.