Objectives: . Idiopathic sudden sensorineural hearing loss (ISSNHL) is a significant cause of hearing loss. Intratympanic steroid injection (ITSI) is commonly used as an initial or salvage treatment; however, the lack of a standardized treatment protocol has resulted in variability in clinical practice. In addition, no efficient prediction model currently exists to support personalized management. Therefore, this study aimed to develop tailored management strategies for ISSNHL using a machine-learning model. Methods: . This retrospective multicenter cohort study was conducted between January 2015 and December 2020, with data analysis performed between January 2021 and March 2024. Patients were selected based on the International Classification of Diseases, 10th Revision criteria for ISSNHL, along with relevant medication and procedure codes. Patients with pure-tone audiogram results not meeting ISSNHL criteria, better initial hearing in the affected ear, an identifiable etiology, no post-treatment audiogram, or delayed treatment (>6 weeks) were excluded. We included 770 patients diagnosed with ISSNHL who received ITSI. The primary outcome was the area under the receiver operating characteristic curve for prediction performance. Recovery status was determined using the last pure-tone audiogram. Modeling was conducted on the Quanta for Medical Care AI platform using five machine-learning algorithms and a nested cross-validation framework, in which feature selection and hyperparameter tuning were performed in the inner folds and model performance was evaluated in the outer folds. Results: . A random forest classifier outperformed the other models in predicting hearing outcomes, achieving an area under the receiver operating characteristic curve of 0.788. Time to ITSI was the most influential treatment-related factor, with ITSI administered within 10 days of hearing loss being associated with better outcomes. This model can be used to provide personalized prognostic estimates under different treatment protocols. Conclusion . The machine-learning-based prediction model facilitates personalized treatment strategies and timely treatment adjustments for ISSNHL, thereby optimizing the likelihood of complete recovery.

Background: Obesity is a well-known risk factor for chronic kidney disease and its progression. However, the impact of obesity on the renal function of the elderly population is uncertain. We investigated the association between obesity and renal outcomes in the elderly. Methods: We analyzed 130,504 participants from the Korean National Health Insurance Service-Senior cohort. Obesity was classified according to body mass index (BMI), sex-specific waist circumference (WC), and the presence of metabolic syndrome. The primary outcome was renal function decline, defined as a decline in the estimated glomerular filtration rate (eGFR) of at least 50% from baseline or new-onset end-stage renal disease. Results: During a follow-up period of 559,531.1 person-years (median, 4.3 years), 2,486 participants (19.0%; incidence rate of 4.44 per 1,000 person-years) showed renal function decline. A multivariate Cox proportional hazards model revealed that BMI/WC was not associated with renal function decline. However, the group with metabolic syndrome had a significantly increased risk of renal function decline compared to the group without metabolic syndrome (adjusted hazard ratio [HR], 1.24; 95% confidence interval [CI], 1.13–1.36). Compared with the non-metabolic syndrome group, the adjusted HRs (95% CI) for participants with one through five components were 0.96 (0.84–1.11), 1.10 (0.96–1.27), 1.24 (1.06–1.45), 1.37 (1.12–1.66), and 1.99 (1.42–2.79), respectively (p for trend < 0.001). Conclusion In elderly Korean adults, metabolic syndrome and the number of its components were associated with a higher risk of renal function decline, but BMI or WC was not significant.

Background: This study examines whether stronger corporate governance is associated with workplace mental health practices and whether this relationship is mediated by a structured sequence of occupational safety and health (OSH) engagement: recognition, goal-setting, and implementation. Methods: We analyzed 134 listed companies in Taiwan that published sustainability reports and received corporate governance evaluations between 2014 and 2023. Governance scores―based on shareholder rights, board functioning, transparency, and sustainability―were dichotomized into high vs. low categories. Workplace mental health practices were measured using 24 binary indicators across planning, provision, and reporting. OSH engagement was conceptualized in three stages based on Global Reporting Initiative 403 guidelines, with composite indicators derived via grouped weighted quantile sum regression. Associations were analyzed using generalized linear mixed-effects and serial mediation models. Results: Compared to companies ranked low in corporate governance, those ranked high had a higher likelihood of adopting mental health practices. OSH goal-setting and implementation showed significant positive associations, whereas recognition did not. Serial mediation analysis showed that 83% of the total effect of corporate governance on mental health practices was direct, while 17% was mediated through structured OSH engagement, primarily via implementation. Recognition showed a nonsignificant effect but initiated significant sequential pathways via goal-setting and implementation.Goal-setting functioned as a bridge within these chains. Conclusion Corporate governance plays a central role in advancing workplace mental health practices.While most effects are direct, structured OSH engagement helps translate governance priorities into sustained organizational actions that embed mental health into routine practice.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

6-gingerol, a bioactive compound from ginger, has demonstrated promising anticancer properties across various cancer models by inducing apoptosis and inhibiting cell proliferation and invasion. In this study, we explore its mechanisms against glioblastoma multiforme (GBM), a notably aggressive and treatment-resistant brain tumor. We found that 6-gingerol crosses the blood-brain barrier more effectively than curcumin, enhancing its potential as a therapeutic agent for brain tumors. Our experiments show that 6-gingerol reduces cell proliferation and triggers apoptosis in GBM cell lines by disrupting cellular energy homeostasis. This process involves an increase in mitochondrial reactive oxygen species (mtROS) and a decrease in mitochondrial membrane potential, primarily due to the downregulation of manganese superoxide dismutase (MnSOD). Additionally, 6-gingerol reduces ERK phosphorylation by inhibiting EGFR and RAF, leading to G1 phase cell cycle arrest. These findings indicate that 6-gingerol promotes cell death in GBM cells by modulating MnSOD and ROS levels and arresting the cell cycle through the ERFR-RAF-1/MEK/ ERK signaling pathway, highlighting its potential as a therapeutic agent for GBM and setting the stage for future clinical research.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background: Delivering a poor prognosis to patients and their families is critically challenging in pediatric populations. The application of palliative care (PC) provides a bridge between accepting the occurrence of mortality and offering lifelong support.However, little is known about the specifics of PC. This study aims to explore the unmet need for PC in pediatric populations. Methods: We retrospectively reviewed the medical records of mortality cases in the Department of Pediatric Hematology and Oncology at Chang Gung Memorial Hospital. Statistical tests, including Chi-square and Student’s t-tests, were applied to determine the differences between early and late intervention groups in terms of the timing of PC introduction. Results: During the study period, 41 patients were included. Their median age was 11.8 years (IQR, 7.6-15.9). The majority of the disease statuses were refractory or relapsing (R/R). The incidence of memento application was significantly higher in the early intervention group (47.6% vs. 10%, P=0.0081). Vital signs variations tended to be end-of-life (EoL) indicators in this study. Conclusion The early introduction of PC encourages families to accompany their beloved child. EoL signs in the pediatric population include vital sign variations. With the presence of relevant EoL signs, clinical physicians can apply PC earlier to meet the needs.

Traumatic brain injury (TBI) is intricately linked to the most severe clinical manifestations of brain damage. It encompasses dynamic pathological mechanisms, including hemodynamic disorders, excitotoxic injury, oxidative stress, mitochondrial dysfunction, inflammation, and neuronal death. This review provides a comprehensive analysis and summary of biomaterial-based tissue engineering scaffolds and nano-drug delivery systems. As an example of functionalized biomaterials, nano-drug delivery systems alter the pharmacokinetic properties of drugs. They provide multiple targeting strategies relying on factors such as morphology and scale, magnetic fields, pH, photosensitivity, and enzymes to facilitate the transport of therapeutics across the blood-brain barrier and to promote selective accumulation at the injury site. Furthermore, therapeutic agents can be incorporated into bioscaffolds to interact with the biochemical and biophysical environment of the brain. Bioscaffolds can mimic the extracellular matrix environment, regulate cellular interactions, and increase the effectiveness of local treatments following surgical interventions. Additionally, stem cell-based and exosome-dominated extracellular vesicle carriers exhibit high bioreactivity and low immunogenicity and can be used to design therapeutic agents with high bioactivity. This review also examines the utilization of endogenous bioactive materials in the treatment of TBI.