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The reliability of adhesive bond strength influences the applicability of structural adhesives in industries. The statistical probability distribution of the strength of adhesive joints is an essential indicator when designing and choosing adhesives. In this study, we experimentally studied the probability distribution of the strength of adhesive joints. The strength data were collected using a novel high-throughput technique consisting of a sample preparation method and shear testing device. Numerous cylindrical butt shear-joint specimens were prepared by mechanical machining and tested using a self-developed shear testing device. The effect of moisture absorption by the adhesive was specifically considered. The probability distribution of the shear strength of the epoxy adhesive joints was characterized using five probability distribution functions: Normal, Lognormal, Exponential, Weibull, and Gamma. Quantile – quantile plots were employed to determine the suitability of each distribution function. The results suggested that the Weibull and Normal distributions were best suited for describing the probability distribution of the strength of the epoxy adhesive joints. The Weibull distribution is particularly suitable for brittle epoxy adhesives. Moisture absorption reduced both the mean and variance of the shear strength, which might be attributed to the plasticization of the adhesives. The high-throughput method proposed in this study significantly improved the efficiency of testing adhesive joints. It not only contributes to the study of the strength distributions of adhesive joints but also helps to shorten the research and development cycle of new adhesives by facilitating rapid strength evaluation. [ABSTRACT FROM AUTHOR]

Recurrent laryngeal nerve palsy remains a significant complication following minimally invasive esophagectomy for esophageal cancer. Despite advancements in surgical techniques and lymphadenectomy precision, the incidence of recurrent laryngeal nerve palsy has not been improved. Recurrent laryngeal nerve palsy predominantly affects the left side and may lead to unilateral or bilateral vocal cord paralysis, resulting in hoarseness, dysphagia, and an increased risk of aspiration pneumonia. While most cases of recurrent laryngeal nerve palsy are temporary and resolve within 6 to 12 months, some patients may experience permanent nerve dysfunction, severely impacting their quality of life. Prevention strategies, such as nerve integrity monitoring, robotic-assisted minimally invasive esophagectomy, and advanced dissection techniques, aim to minimize nerve injury, though their effectiveness varies. The management of recurrent laryngeal nerve palsy includes voice and swallowing rehabilitation, reinnervation techniques, and, in severe cases, surgical interventions such as thyroplasty and intracordal injection. As recurrent laryngeal nerve palsy can lead to significant postoperative respiratory complications, a multidisciplinary approach involving surgical precision, early detection, and comprehensive rehabilitation is crucial to improving patient outcomes and minimizing long-term morbidity in minimally invasive esophagectomy. This review article aims to inform esophageal surgeons and other clinicians about strategies for the prevention and management of recurrent laryngeal nerve palsy in esophagectomy. [ABSTRACT FROM AUTHOR]

To evaluate the peel resistance of a device wherein a polyimide (PI) sheet is bonded to a curved surface with a pressure-sensitive adhesive (PSA) experimentally and analytically, we applied a mechanical simulation considering the viscoelasticity of the pressure-sensitive adhesive (PSA) and the fracture energy of the bonding interface. The mechanical behavior against the local load applied on the PI sheet surface was evaluated using this simulation. To verify the accuracy of this simulation, we fabricated a local load tester that could apply a load to a local area of 8 mm in diameter and compared the mechanical behavior with the simulation results. Dynamic mechanical analysis of the PSA with a film thickness of 19 μm was performed using the nanoindentation technique. The generalized Maxwell model parameters were estimated by fitting the relaxation elastic modulus E(t) to conduct a mechanical simulation. For the double cantilever beam test to derive the interfacial fracture energy, soft PI sheets were reinforced with Al substrates. As a result of these analyses, it was confirmed that the relationship between the displacement and load obtained by this simulation agrees well with the actual local load test. This technique is useful for predicting the bonding strength of complex-shaped devices fabricated using soft adherends and adhesives. [ABSTRACT FROM AUTHOR]

Objectives: High‐resolution manometry (HRM) and esophagography are used for achalasia diagnosis; however, achalasia phenotypes combining esophageal motility and morphology are unknown. Moreover, predicting treatment outcomes of peroral endoscopic myotomy (POEM) in treatment‐naïve patients remains an unmet need. Methods: In this multicenter cohort study, we included 1824 treatment‐naïve patients diagnosed with achalasia. In total, 1778 patients underwent POEM. Clustering by machine learning was conducted to identify achalasia phenotypes using patients' demographic data, including age, sex, disease duration, body mass index, and HRM/esophagography findings. Machine learning models were developed to predict persistent symptoms (Eckardt score ≥3) and reflux esophagitis (RE) (Los Angeles grades A–D) after POEM. Results: Machine learning identified three achalasia phenotypes: phenotype 1, type I achalasia with a dilated esophagus (n = 676; 37.0%); phenotype 2, type II achalasia with a dilated esophagus (n = 203; 11.1%); and phenotype 3, late‐onset type I–III achalasia with a nondilated esophagus (n = 619, 33.9%). Types I and II achalasia in phenotypes 1 and 2 exhibited different clinical characteristics from those in phenotype 3, implying different pathophysiologies within the same HRM diagnosis. A predictive model for persistent symptoms exhibited an area under the curve of 0.70. Pre‐POEM Eckardt score ≥6 was the greatest contributing factor for persistent symptoms. The area under the curve for post‐POEM RE was 0.61. Conclusion: Achalasia phenotypes combining esophageal motility and morphology indicated multiple disease pathophysiologies. Machine learning helped develop an optimal risk stratification model for persistent symptoms with novel insights into treatment resistance factors. [ABSTRACT FROM AUTHOR]

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa. [ABSTRACT FROM AUTHOR]