Case Study of Altruistic Behavior and Relational Network with Business Value on Local Travel Agency

The present study based on a local travel agency in Tainan, investigation how does the altruistic behavior affected the relational network and created the business value. The case company’s CEO had voluntary participated in charity societies more than 20 years. The present study first showed how the case company’s CEO to build an emotional relational network through altruistic behavior. Second, how does the emotional relational network form mixed relational network based on the key features demonstrated by altruistic behavior. Finally, also showed how the mixed relational network is transfer into an instrumental relational network. The results showed that the altruistic behavior can help local travel agency develop and increase their business value via relational network. The key factors to maximize the business value are the professional knowledge and altruistic behavior

Effectiveness and minimum effective dose of app-based mobile health interventions for anxiety and depression symptom reduction: Systematic review and meta-analysis

BACKGROUND: Mobile health (mHealth) apps offer new opportunities to deliver psychological treatments for mental illness in an accessible, private format. The results of several previous systematic reviews support the use of app-based mHealth interventions for anxiety and depression symptom management. However, it remains unclear how much or how long the minimum treatment dose is for an mHealth intervention to be effective. Just-in-time adaptive intervention (JITAI) has been introduced in the mHealth domain to facilitate behavior changes and is positioned to guide the design of mHealth interventions with enhanced adherence and effectiveness. OBJECTIVE: Inspired by the JITAI framework, we conducted a systematic review and meta-analysis to evaluate the dose effectiveness of app-based mHealth interventions for anxiety and depression symptom reduction. METHODS: We conducted a literature search on 7 databases (ie, Ovid MEDLINE, Embase, PsycInfo, Scopus, Cochrane Library (eg, CENTRAL), ScienceDirect, and ClinicalTrials, for publications from January 2012 to April 2020. We included randomized controlled trials (RCTs) evaluating app-based mHealth interventions for anxiety and depression. The study selection and data extraction process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We estimated the pooled effect size using Hedge g and appraised study quality using the revised Cochrane risk-of-bias tool for RCTs. RESULTS: We included 15 studies involving 2627 participants for 18 app-based mHealth interventions. Participants in the intervention groups showed a significant effect on anxiety (Hedge g=-.10, 95% CI -0.14 to -0.06, I2=0%) but not on depression (Hedge g=-.08, 95% CI -0.23 to 0.07, I2=4%). Interventions of at least 7 weeks\u27 duration had larger effect sizes on anxiety symptom reduction. CONCLUSIONS: There is inconclusive evidence for clinical use of app-based mHealth interventions for anxiety and depression at the current stage due to the small to nonsignificant effects of the interventions and study quality concerns. The recommended dose of mHealth interventions and the sustainability of intervention effectiveness remain unclear and require further investigation

Life expectancies and incidence rates of patients under prolonged mechanical ventilation: a population-based study during 1998 to 2007 in Taiwan

[[abstract]]Introduction: The present study examined the median survival, life expectancies, and cumulative incidence rate (CIR) of patients undergoing prolonged mechanical ventilation (PMV) stratified by different underlying diseases.Methods: According to the National Health Insurance Research Database of Taiwan, there were 8,906,406 individuals who obtained respiratory care during the period from 1997 to 2007. A random sample of this population was performed, and subjects who had continuously undergone mechanical ventilation for longer than 21 days were enrolled in the current study. Annual incidence rates and the CIR were calculated. After stratifying the patients according to their specific diagnoses, latent class analysis was performed to categorise PMV patients with multiple co-morbidities into several groups. The life expectancies of different groups were estimated using a semiparametric method with a hazard function based on the vital statistics of Taiwan.Results: The analysis of 50,481 PMV patients revealed that incidence rates increased as patients grew older and that the CIR (17 to 85 years old) increased from 0.103 in 1998 to 0.183 in 2004 before stabilising thereafter. The life expectancies of PMV patients suffering from degenerative neurological diseases, stroke, or injuries tended to be longer than those with chronic renal failure or cancer. Patients with chronic obstructive pulmonary disease survived longer than did those co-morbid with other underlying diseases, especially septicaemia/shock.Conclusions: PMV provides a direct means to treat respiratory tract diseases and to sustain respiration in individuals suffering from degenerative neurological diseases, and individuals with either of these types of conditions respond better to PMV than do those with other co-morbidities. Future research is required to determine the cost-effectiveness of this treatment paradigm

Genetic polymorphisms in glutathione S-transferase (GST) superfamily and risk of arsenic-induced urothelial carcinoma in residents of southwestern Taiwan

<p>Abstract</p> <p>Background</p> <p>Arsenic exposure is an important public health issue worldwide. Dose-response relationship between arsenic exposure and risk of urothelial carcinoma (UC) is consistently observed. Inorganic arsenic is methylated to form the metabolites monomethylarsonic acid and dimethylarsinic acid while ingested. Variations in capacity of xenobiotic detoxification and arsenic methylation might explain individual variation in susceptibility to arsenic-induced cancers.</p> <p>Methods</p> <p>To estimate individual susceptibility to arsenic-induced UC, 764 DNA specimens from our long-term follow-up cohort in Southwestern Taiwan were used and the genetic polymorphisms in GSTM1, GSTT1, GSTP1 and arsenic methylation enzymes including GSTO1 and GSTO2 were genotyped.</p> <p>Results</p> <p>The GSTT1 null was marginally associated with increased urothelial carcinoma (UC) risk (HR, 1.91, 95% CI, 1.00-3.65), while the association was not observed for other GSTs. Among the subjects with cumulative arsenic exposure (CAE) ≥ 20 mg/L*year, the GSTT1 null genotype conferred a significantly increased cancer risk (RR, 3.25, 95% CI, 1.20-8.80). The gene-environment interaction between the GSTT1 and high arsenic exposure with respect to cancer risk was statistically significant (multiplicative model, <it>p </it>= 0.0151) and etiologic fraction was as high as 0.86 (95% CI, 0.51-1.22). The genetic effects of GSTO1/GSTO2 were largely confined to high arsenic level (CAE ≥ 20). Diplotype analysis showed that among subjects exposed to high levels of arsenic, the AGG/AGG variant of GSTO1 Ala140Asp, GSTO2 5'UTR (-183)A/G, and GSTO2 Asn142Asp was associated with an increased cancer risk (HRs, 4.91, 95% CI, 1.02-23.74) when compared to the all-wildtype reference, respectively.</p> <p>Conclusions</p> <p>The GSTs do not play a critical role in arsenic-induced urothelial carcinogenesis. The genetic effects of GSTT1 and GSTO1 on arsenic-induced urothelial carcinogenesis are largely confined to very high exposure level.</p

Natural Product Chemistry of Gorgonian Corals of Genus Junceella—Part II

The structures, names, bioactivities, and references of 81 new secondary metabolites obtained from gorgonian corals belonging to the genus Junceella are described in this review. All compounds mentioned in this review were obtained from sea whip gorgonian corals Junceella fragilis and Junceella juncea, collected from the tropical and subtropical Indo-Pacific Ocean

Chronic cisplatin treatment promotes enhanced damage repair and tumor progression in a mouse model of lung cancer

Chemotherapy resistance is a major obstacle in cancer treatment, yet the mechanisms of response to specific therapies have been largely unexplored in vivo. Employing genetic, genomic, and imaging approaches, we examined the dynamics of response to a mainstay chemotherapeutic, cisplatin, in multiple mouse models of human non-small-cell lung cancer (NSCLC). We show that lung tumors initially respond to cisplatin by sensing DNA damage, undergoing cell cycle arrest, and inducing apoptosis—leading to a significant reduction in tumor burden. Importantly, we demonstrate that this response does not depend on the tumor suppressor p53 or its transcriptional target, p21. Prolonged cisplatin treatment promotes the emergence of resistant tumors with enhanced repair capacity that are cross-resistant to platinum analogs, exhibit advanced histopathology, and possess an increased frequency of genomic alterations. Cisplatin-resistant tumors express elevated levels of multiple DNA damage repair and cell cycle arrest-related genes, including p53-inducible protein with a death domain (Pidd). We demonstrate a novel role for PIDD as a regulator of chemotherapy response in human lung tumor cells.National Institutes of Health (U.S.) (grant 5-UO1-CA84306)National Cancer Institute (U.S.) (CA034992

Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care

ObjectiveTo implement an all-day online artificial intelligence (AI)-assisted detection of ST-elevation myocardial infarction (STEMI) by prehospital 12-lead electrocardiograms (ECGs) to facilitate patient triage for timely reperfusion therapy.MethodsThe proposed AI model combines a convolutional neural network and long short-term memory (CNN-LSTM) to predict STEMI on prehospital 12-lead ECGs obtained from mini-12-lead ECG devices equipped in ambulance vehicles in Central Taiwan. Emergency medical technicians (EMTs) from the 14 AI-implemented fire stations performed the on-site 12-lead ECG examinations using the mini portable device. The 12-lead ECG signals were transmitted to the AI center of China Medical University Hospital to classify the recordings as “STEMI” or “Not STEMI”. In 11 non-AI fire stations, the ECG data were transmitted to a secure network and read by available on-line emergency physicians. The response time was defined as the time interval between the ECG transmission and ECG interpretation feedback.ResultsBetween July 17, 2021, and March 26, 2022, the AI model classified 362 prehospital 12-lead ECGs obtained from 275 consecutive patients who had called the 119 dispatch centers of fire stations in Central Taiwan for symptoms of chest pain or shortness of breath. The AI's response time to the EMTs in ambulance vehicles was 37.2 ± 11.3 s, which was shorter than the online physicians' response time from 11 other fire stations with no AI implementation (113.2 ± 369.4 s, P &lt; 0.001) after analyzing another set of 335 prehospital 12-lead ECGs. The evaluation metrics including accuracy, precision, specificity, recall, area under the receiver operating characteristic curve, and F1 score to assess the overall AI performance in the remote detection of STEMI were 0.992, 0.889, 0.994, 0.941, 0.997, and 0.914, respectively. During the study period, the AI model promptly identified 10 STEMI patients who underwent primary percutaneous coronary intervention (PPCI) with a median contact-to-door time of 18.5 (IQR: 16–20.8) minutes.ConclusionImplementation of an all-day real-time AI-assisted remote detection of STEMI on prehospital 12-lead ECGs in the field is feasible with a high diagnostic accuracy rate. This approach may help minimize preventable delays in contact-to-treatment times for STEMI patients who require PPCI