The COVID-19 pandemic has become a serious concern and has negatively impacted public health and the economy. It primarily targets the lungs, causing acute respiratory distress syndrome (ARDS); however, it may also lead to multiple organ failure (MOF) and enhanced mortality rates. Hence, there is an urgent need to develop potential effective therapeutic strategies for COVID-19 patients. Extracellular vesicles (EVs) are released from various types of cells that participate in intercellular communication to maintain physiological and pathological processes. EVs derived from various cellular origins have revealed suppressive effects on the cytokine storm during systemic hyper-inflammatory states of severe COVID-19, leading to enhanced alveolar fluid clearance, promoted epithelial and endothelial recovery, and cell proliferation. Being the smallest subclass of EVs, exosomes offer striking characteristics such as cell targeting, being nano-carriers for drug delivery, high biocompatibility, safety, and low-immunogenicity, thus rendering them a potential cell-free therapeutic candidate against the pathogeneses of various diseases. Due to these properties, numerous studies and clinical trials have been performed to assess their safety and therapeutic efficacy against COVID-19. Hence, in this review, we have comprehensively described current updates on progress and challenges for EVs as a potential therapeutic agent for the management of COVID-19.

Transforaminal lumber interbody fusion (TLIF) is the last resort to address the lumber degenerative disorders such as spondylolisthesis, causing lower back pain. The current surgical intervention for these abnormalities includes open TLIF. However, in recent years, minimally invasive TLIF (MIS-TLIF) has gained a high momentum, as it could minimize the risk of infection, blood loss, and post-operative complications pertaining to fusion surgery. Further advancement in visualizing and guiding techniques along with grafting cage and materials are continuously improving the safety and efficacy of MIS-TLIF. These assistive techniques are also playing a crucial role to increase and improve the learning curve of surgeons. However, achieving an appropriate output through TLIF still remains a challenge, which might be synergized through 3D-printing and tissue engineering-based regenerative therapy. Owing to their differentiation potential, biomaterials such as stem/progenitor cells may contribute to restructuring lost or damaged tissues during MIS-TLIF, and this therapeutic efficacy could be further supplemented by platelet-derived biomaterials, leading to improved clinical outcomes. Thus, based on the above-mentioned strategies, we have comprehensively summarized recent developments in MIS-TLIF and its possible combinatorial regenerative therapies for rapid and long-term relief.

Background: The global nursing shortage, exacerbated by heavy workloads and high turnover rates associated with the COVID-19 pandemic, continues to undermine care quality and nurse well-being. Although digital health technologies have enhanced coordination, improved communication, and reduced clinical errors in nursing practice, they have also increased nurses’ documentation burden. Advances in large language models (LLMs) and other generative artificial intelligence (GenAI) tools facilitate the generation of accurate reports from electronic medical records (EMRs), thereby streamlining documentation workflows, saving time, and reducing nurses’ workloads. Accordingly, integrating LLMs into electronic nursing documentation systems warrants further exploration. Objective: This study examines the integration of an LLM into an in-house nursing information system (NIS) implemented across 3 hospitals in Taiwan to reduce the time and effort required for nursing handover documentation and to preliminarily assess the operational and economic implications of GenAI-assisted workflows. Methods: A multidisciplinary team of nursing specialists and information technology experts at Taipei Medical University (TMU) restructured the organization’s existing nursing handover documentation process to facilitate interaction with the LLM. The team also developed prompt-based interfaces to automatically generate section-specific content for the nursing handover document. The LLM-integrated NIS was subsequently deployed across 3 hospitals in Taiwan: Taipei Medical University Hospital (TMUH), Wan Fang Hospital (WFH), and Shuang Ho Hospital (SHH). We then extracted and analyzed NIS log data to compare documentation times before and after LLM implementation, thereby quantifying time savings. Results: Integration of the LLM into nursing handover documentation was associated with shorter per-patient documentation time in routine clinical use across TMUH, WFH, and SHH. Based on preintegration NIS logs (September 2024), the average handover document completion time per patient ranged from 3.45 (SD 3.82) to 4.32 (SD 4.48) minutes across hospitals and shifts, providing a preliminary baseline for subsequent comparisons. In postintegration NIS logs (October-December 2024), the overall handover document completion time per patient (mean) was substantially lower, ranging from 1.17 (SD 1.86) to 2.54 (SD 2.82) minutes across hospitals and shifts. Using monthly patient volume to estimate time savings, 113-273, 160-314, and 198-391 hours were saved per month at TMUH, WFH, and SHH, respectively, corresponding to aggregate savings of 474-981 hours per month across hospitals during the study period. Conclusions: We integrated an LLM into an NIS to generate nursing handover documents without altering existing workflows. Across 3 hospitals within TMU’s health system, GenAI assistance was associated with shorter documentation time and a positive net labor value from October to December 2024. Prompts were constrained, and nurse verification was required to mitigate hallucinations. Future work will enhance logging to capture reliability and editing metrics, compare LLM-generated drafts with nurse-finalized notes to inform prompt refinement, and assess generalizability to other documentation workflows.

Background Relevant clinical studies have been small and have not convincingly demonstrated whether the perioperative initiation of beta‐blockers should be considered in patients with diabetes mellitus undergoing noncardiac surgery. Methods and Results In this nationwide propensity score–matched study, we included patients with diabetes mellitus undergoing noncardiac surgery between 2000 and 2011 from Taiwan's National Health Insurance Research Database. Patients were classified as beta‐blocker and non–beta‐blocker cohorts. We further stratified beta‐blocker users into cardioprotective beta‐blocker (atenolol, bisoprolol, metoprolol, or carvedilol) and other beta‐blocker users. To investigate time of initiation of beta‐blocker use, initiation time was stratified into 2 periods (>30 and ≤30 days preoperatively). The outcomes of interest were in‐hospital and 30‐day mortality. After propensity score matching, we identified 50 952 beta‐blocker users and 50 952 matched controls. Compared with non–beta‐blocker users, cardioprotective beta‐blocker users were associated with lower risks of in‐hospital (odds ratio 0.75, 95% CI 0.68–0.82) and 30‐day (odds ratio 0.75, 95% CI 0.70–0.81) mortality. Among initiation times, only the use of cardioprotective beta‐blockers for >30 days was associated with decreased risk of in‐hospital (odds ratio 0.72, 95% CI 0.65–0.78) and 30‐day (odds ratio 0.72, 95% CI 0.66–0.78) mortality. Of note, use of other beta‐blockers for ≤30 days before surgery was associated with increased risk of both in‐hospital and 30‐day mortality. Conclusions The use of cardioprotective beta‐blockers for >30 days before surgery was associated with reduced mortality risk, whereas short‐term use of beta‐blockers was not associated with differences in mortality in patients with diabetes mellitus.

Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR.