Since first reported in 2002, the rate of methicillin-resistant Staphylococcus aureus (MRSA) among childhood community-associated (CA) S. aureus infection in Taiwan increased significantly up to 2005. There have been no reports on this issue since then. We prospectively collected clinical S. aureus isolates from the patients <19 years of age in a university-affiliated hospital in 2012. Only first isolate from each patient was included. The medical records were retrospectively reviewed and the patients were classified as CA or healthcare-associated (HA) by the standard epidemiologic criteria. Isolates as CA-MRSA were further characterized by pulsed-field gel electrophoresis, staphylococcal cassette chromosome (SCCmec) typing, and multilocus sequence typing. A total of 409 S. aureus isolates were included, and 260 (63.6%) were MRSA. The proportion of MRSA among all S. aureus isolates in 2012 increased significantly (p < 0.001) compared to that in 2004–2005. Of the 181 CA-MRSA isolates, 86.2% were identified from pus or wound. Nine pulsotypes were identified with two major types (type D, 119 (65.7%); type C, 27 (14.9%). Most of the isolates carried either SCCmec IV (66 isolates, 36%) or VT (112 isolates, 62%). 128 isolates (71%) carried Panton-Valentine leukocidin (PVL) genes. Clonal complex (CC) 59 accounted for 146 isolates (80.7%) of two major pulsotypes, CC45 for 19 isolates, ST30 for 6 isolates and ST8 (USA 300) for 4 isolates. In addition to penicillin (100%), most isolates were resistant to erythromycin (81%) and clindamycin (79.3%). Around two-thirds of childhood community-associated S. aureus infections in northern Taiwan were MRSA. Though CC59 is still the prevalent community clone, several new clones emerged in northern Taiwan.

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large‐dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient‐based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

Background/Objectives: The precise localization of cervical vertebrae in X-ray imaging was essential for effective diagnosis and treatment planning, particularly as the prevalence of cervical degenerative conditions increased with an aging population. Vertebrae from C2 to C7 were commonly affected by disorders such as ossification of the posterior longitudinal ligament (OPLL) and nerve compression caused by posterior osteophytes, necessitating thorough evaluation. However, manual annotation remained a major aspect of traditional clinical procedures, making it challenging to manage increasing patient volumes and large-scale medical imaging data. Methods: To address this issue, this study presented an automated approach for localizing cervical vertebrae and measuring neural foraminal distance. The proposed technique analyzed the neural foramen distance and intervertebral space using image enhancement to determine the degree of nerve compression. YOLOv8 was employed to detect and segment the cervical vertebrae. Moreover, by integrating automated cervical spine analysis with advanced imaging technologies, the system enabled rapid detection of abnormal intervertebral disc gaps, facilitating early identification of degenerative changes. Results: According to the results, the system achieved a spine localization accuracy of 99.5%, representing an 11.7% improvement over existing approaches. Notably, it outperformed previous methods by 66.67% in recognizing the C7 vertebra, achieving a perfect 100% accuracy. Conclusions: Furthermore, the system significantly streamlined the diagnostic workflow by processing each X-ray image in just 17.9 milliseconds. This approach markedly improved overall diagnostic efficiency.

The optimal timing of surgical decompression for traumatic spinal cord injury (TSCI) remains uncertain. This study evaluated the association between ultra-early decompression and neurological outcomes, focusing on early post-injury time windows. Retrospective cohort study. Single tertiary trauma center. Eighty-two adult patients with TSCI who underwent surgical decompression between January 2022 and August 2024 were included. Patients with central cord syndrome, penetrating injury, incomplete imaging, follow-up <3 months, or unreliable neurological assessment were excluded. Surgical decompression at varying time thresholds (≤8, ≤10, ≤12, and ≤24 h after injury). Neurological improvement, defined as an increase of at least one AIS grade at ≥3 months. In AIS A patients, decompression within 8 h was associated with a higher rate of neurological improvement (87.5% vs 29.4%; OR 16.80, 95% CI 1.62-174.52). Multivariable analysis showed a substantial but non-significant effect for ≤8 h (aOR 4.99, 95% CI 0.83-29.95). A strong adjusted association was observed at ≤10 h (aOR 4.92, 95% CI 1.15-20.97). Shock was associated with lower odds of improvement. The most consistent associations were observed within the 8-12 h window. Earlier decompression may be associated with improved neurological outcomes after TSCI. The findings suggest that the therapeutic window for neurological recovery may lie within the first 8-12 h, although causal inference cannot be established.