Artificial Intelligence-Empowered Spectroscopic Single Molecule Localization Microscopy.

Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials.

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms.

Investigation of out-of-distribution detection across various models and training methodologies.

Machine learning-based algorithms demonstrate impressive performance across numerous fields; however, they continue to suffer from certain limitations. Even sophisticated and precise algorithms often make erroneous predictions when implemented with datasets having different distributions compared to the training set. Out-of-distribution (OOD) detection, which distinguishes data with different distributions from that of the training set, is a critical research area necessary to overcome these limitations and create more reliable algorithms.

Bacteria detection and species identification at the single-cell level using super-resolution fluorescence imaging and AI analysis.

The skin microbiome is thought to play a critical role in maintaining skin health and protecting against infection. While most microorganisms that live on the skin are harmless or even beneficial, some can cause skin infections or other health problems, emphasizing the importance of diagnosis of the composition and diversity of the skin flora. However, conventional diagnostic methods for evaluation of the skin microbiome are not sensitive enough to detect bacteria at low concentrations and suffer from poor specificity, thus limiting early diagnosis of bacterial infections.

Onlay patch augmentation in rotator cuff repair for moderate to large tears in elderly patients: clinical and radiologic outcomes.

Background: This study evaluated the clinical and radiologic outcomes of onlay patch augmentation in rotator cuff repair for moderate-to-large tears in elderly patients.

Methods: We reviewed 24 patients who underwent onlay augmentation with dermal allograft after arthroscopic rotator cuff repair from January 2017 to March 2020. Inclusion criteria were patients aged >65 years with tears >2.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images.

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods.

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis.

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data.

Unrecognized bony Bankart lesion accompanying a dislocated four-part proximal humerus fracture before surgery: a case report.

Proximal humerus fractures are the third most common fractures, totaling 4% to 5% of all fractures. Here, we present the case of a 39-yearold man with a dislocated four-part fracture of the proximal humerus with a huge bony Bankart lesion. Preoperatively, the bony Bankart lesion of the glenoid was not visualized on computed tomography scans or magnetic resonance imaging because the fracture of the proximal humerus was comminuted, displaced, and complex.

Complications and Frequency of Surgical Treatment with AO-Type Hook Plate in Shoulder Trauma: A Retrospective Study.

We investigated the complications and frequency of hook plate fixation in patients with shoulder trauma. We reviewed 216 cases of hook plate fixation use at our hospital between January 2010 and May 2020. Finally, we included 76 cases of acute distal clavicle fracture (DCF) and 84 cases of acute acromioclavicular joint dislocation (ACD).

How to handle noisy labels for robust learning from uncertainty.

Most deep neural networks (DNNs) are trained with large amounts of noisy labels when they are applied. As DNNs have the high capacity to fit any noisy labels, it is known to be difficult to train DNNs robustly with noisy labels. These noisy labels cause the performance degradation of DNNs due to the memorization effect by over-fitting.

Strut Support with Tricortical Iliac Allografts in Unstable Proximal Humerus Fractures: Surgical Indication and New Definition of Poor Medial Column Support.

Background: The execution of fibular allograft augmentation in unstable proximal humerus fractures (PHFs) was technically demanding. In this study, the authors evaluated the clinical and radiographic outcomes after tricortical iliac allograft (TIA) augmentation in PHFs.

Methods: We retrospectively assessed 38 PHF patients treated with locking-plate fixation and TIA augmentation.

Surgical results of intradural extramedullary tumors.

Background: To report the treatment results of 12 patients who underwent a total excision of intradural extramedullary tumors.

Methods: Twelve cases of histopathologically confirmed intradural extramedullary tumors were treated surgically between February 2002 and March 2005. There were 8 males and 4 females with an average age of 42.