PURPOSE: The purpose of this study was to understand the situation of diabetes patients receiving examinations for diabetes complications and to explore the factors influencing their intention to receive examinations for diabetes complications. METHODS: A cross-sectional study was performed that included 251 diabetes patients who visited outpatient clinics in Southern Taiwan. A survey using a self-administered questionnaire was conducted from October 2015 to January 2016. The questionnaire included items on demographic characteristics, perceived susceptibility to diabetes complications, perceived seriousness of diabetes complications, perceived benefits of taking action to receive diabetes complication examinations, perceived barriers to taking action to receive diabetes complication examinations, and the intention to receive diabetes complication examinations. The data were analyzed using regression analysis. RESULTS: The percentage of participants who received fundus, foot, and kidney examinations was 67.7%, 61.4%, and 73.3%, respectively. Every point increase on the perceived barriers to taking action to receive diabetes complication examinations scale increased the intention to receive a foot examination in the following year by 0.91 times (p = .002), and every point increase on the perceived susceptibility to diabetes complications scale increased the intention to receive a kidney examination in the following year by 1.19 times (p = .045). CONCLUSIONS: Nurses should shoulder the responsibility to increase patients' intention to receive examination of diabetes complications. The results of this study can be used to promote nurses' care efficacy in preventing diabetes complications. They can also provide medical institutions with information to establish prevention and control policies for diabetes complications.
Ambulatory Care/utilization ; Cross-Sectional Studies ; Diabetic Angiopathies/nursing/*prevention & control/psychology ; Diabetic Nephropathies/nursing/*prevention & control/psychology ; Disease Susceptibility/psychology ; Early Diagnosis ; Female ; Humans ; Intention ; Kidney Function Tests ; Male ; Middle Aged ; Nurse-Patient Relations ; Ophthalmoscopy ; Patient Acceptance of Health Care/*psychology ; Perception ; Physical Examination/nursing/*psychology/utilization ; Taiwan

Purpose: Nowadays most children with congenital heart disease (CHD) are expected to survive to adulthood. The healthcare focus needs to pay close attention to the important developmental tasks during their growth process. The women with CHD face some challenges in their critically developmental stages during pregnancy, delivery, and even motherhood. The lived experience of being a mother needs to be further concerned. This study aimed to explore the lived experience of first-time mothers with CHD. Methods: Descriptive phenomenological design was adopted. Semi-structured interviews were conducted from April to August 2018 with 11 primiparous women with CHD, who were recruited from the pediatric and adult cardiology outpatient departments at a medical center and who had a child aged between 6 months and 3 years. Giorgi's phenomenological analysis method was employed. Results: Six main themes arose from the analysis: (1) recognizing pregnancy risks, (2) performing self-care for health, (3) building self-worth from my baby, (4) adapting to postpartum life and adjusting priorities, (5) enjoying being a first-time mother, and (6) the factors contributing to success in high-risk childbirth. Conclusions The experiences that occurred prior to and after labor that were identified in this study can assist women with CHD to more capably prepare for and understand the process of becoming a mother, including recognition of the importance of a prepregnancy evaluation. The findings of this study can help women with CHD to better understand the path to becoming a mother and prepare themselves for the challenges that lie ahead.

Background: Feline mammary carcinoma (FMC) is one of the most prevalent malignancies of female cats. FMC is highly metastatic and thus leads to poor disease outcomes. Among all metastases, liver metastasis occurs in about 25% of FMC patients. However, the mechanism underlying hepatic metastasis of FMC remains largely uncharacterized. Results: Herein, we demonstrate that FMC-derived extracellular vesicles (FMC-EVs) promotes the liver metastasis of FMC by activating hepatic stellate cells (HSCs) to prime a hepatic premetastatic niche (PMN). Moreover, we provide evidence that sphingosine kinase 1 (SK1) delivered by FMC-EV was pivotal for the activation of HSC and the formation of hepatic PMN. Depletion of SK1 impaired cargo sorting in FMC-EV and the EV-potentiated HSC activation, and abol‑ ished hepatic colonization of FMC cells. Conclusions Taken together, our findings uncover a previously uncharacterized mechanism underlying liver-metas‑ tasis of FMC and provide new insights into prognosis and treatment of this feline malignancy.

Objective: Predicting disease relapse and early intervention could reduce symptom severity. We attempted to identify potential indicators that predict the duration to next admission for an acute affective episode in patients with bipolar I disorder. Methods: We mathematically defined the duration to next psychiatric admission and performed single-variate regressions using historical data of 101 patients with bipolar I disorder to screen for potential variables for further multivariate regressions. Results: Age of onset, total psychiatric admissions, length of lithium use, and carbamazepine use during the psychiatric hospitalization contributed to the next psychiatric admission duration positively. The all-in-one found that hyperlipidemia during the psychiatric hospitalization demonstrated a negative contribution to the duration to next psychiatric admission; the last duration to psychiatric admission, lithium and carbamazepine uses during the psychiatric hospitalization, and heart rate on the discharge day positively contributed to the duration to next admission. Conclusion We identified essential variables that may predict the duration of bipolar I patients’ next psychiatric admission. The correlation of a faster heartbeat and a normal lipid profile in delaying the next onset highlights the importance of managing these parameters when treating bipolar I disorder.

Objective: To assess whether carbon monoxide (CO) poisoning increases the incidence of dementia. Methods: We searched the Cochrane Library, PubMed, and EMBASE from inception to 14 August 2022. Two authors independently selected studies, assessed the quality of included studies, and extracted data. Any disagreement was resolved by discussion with a third author. Only cohort study with an enough follow-up period was included for systematic reviews and meta-analysis. Results: Thirty-three full texts were initially searched, but only three studies met our inclusion criteria, and they were comprised of 134,563 participants who were initially free of dementia. The follow-up period ranged from 9 to 12 years. We found that CO poisoning increased the risk of dementia incidence (adjusted hazard ratio 2.61, 95% confidence interval 1.56 to 4.36, p=0.0003). Subgroup analysis showed that the increased dementia risk was significant in males but not in females, and the highest risk was in young age group, followed by in middle age group, but not in the old one. Conclusion Overall the evidence from prospective cohort studies supported a link between CO exposure and an increased dementia risk, although all the included studies were limited to Taiwanese population.

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.

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.