A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks.

The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to in-flight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.

Survival After Invasive or Conservative Management of Stable Coronary Disease.

Background: The ISCHEMIA trial compared an initial invasive versus an initial conservative management strategy for patients with chronic coronary disease and moderate or severe ischemia, with no major difference in most outcomes over a median of 3.2 years. Extended follow-up for mortality is ongoing. Methods: ISCHEMIA participants were randomized to an initial invasive strategy (INV) added to guideline-directed medical therapy or a conservative strategy (CON). Patients with moderate or severe ischemia, ejection fraction ≥35%, and no recent acute coronary syndromes were included. Those with an unacceptable level of angina were excluded. Extended follow-up for vital status is being conducted by sites or through central death index search. Data obtained through December 2021 are included in this interim report. We analyzed all-cause, cardiovascular, and non-cardiovascular mortality by randomized strategy, using nonparametric cumulative incidence estimators, Cox regression models and Bayesian methods. Undetermined deaths were classified as cardiovascular as pre-specified in the trial protocol. Results: Baseline characteristics for 5179 original ISCHEMIA trial participants included median age 65 years, 23 % women, 16% Hispanic, 4% Black, 42% diabetes, and median EF 0.60. A total of 557 deaths accrued over a median follow-up of 5.7 years, with 268 of these added in the extended follow-up phase. This included a total of 343 cardiovascular deaths, 192 non-cardiovascular deaths and 22 unclassified deaths. All-cause mortality was not different between randomized treatment groups (7-year rate 12.7% in INV, 13.4% in CON; adjusted hazard ratio (HR)=1.00, 95% CI: 0.85-1.18). There was a lower 7-year rate cardiovascular mortality (6.4% vs. 8.6%, adjusted HR=0.78, 95% CI: 0.63-0.96) with an initial invasive strategy but a higher 7-year rate of non-cardiovascular mortality (5.6% vs. 4.4%, adjusted HR=1.44, 95% CI: 1.08-1.91) compared with the conservative strategy. No heterogeneity of treatment effect was evident in prespecified subgroups, including multivessel coronary disease. Conclusions: There was no difference in all-cause mortality with an initial invasive strategy compared with an initial conservative strategy, but there was lower risk of cardiovascular mortality and higher risk of non-cardiovascular mortality with an initial invasive strategy over a median follow-up of 5.7 years. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT04894877; https://clinicaltrials.gov/ct2/show/NCT04894877.

Predicting the behavioral intentions of hospice and palliative care providers from real-world data using supervised learning: A cross-sectional survey study.

Background: Hospice and palliative care (HPC) aims to improve end-of-life quality and has received much more attention through the lens of an aging population in the midst of the coronavirus disease pandemic. However, several barriers remain in China due to a lack of professional HPC providers with positive behavioral intentions. Therefore, we conducted an original study introducing machine learning to explore individual behavioral intentions and detect factors of enablers of, and barriers to, excavating potential human resources and improving HPC accessibility. Methods: A cross-sectional study was designed to investigate healthcare providers' behavioral intentions, knowledge, attitudes, and practices in hospice care (KAPHC) with an indigenized KAPHC scale. Binary Logistic Regression and Random Forest Classifier (RFC) were performed to model impacting and predict individual behavioral intentions. Results: The RFC showed high sensitivity (accuracy = 0.75; F1 score = 0.84; recall = 0.94). Attitude could directly or indirectly improve work enthusiasm and is the most efficient approach to reveal behavioral intentions. Continuous practice could also improve individual confidence and willingness to provide HPC. In addition, scientific knowledge and related skills were the foundation of implementing HPC. Conclusion: Individual behavioral intention is crucial for improving HPC accessibility, particularly at the initial stage. A well-trained RFC can help estimate individual behavioral intentions to organize a productive team and promote additional policies.

Anxiety and depression in dry eye patients during the COVID-19 pandemic: Mental state investigation and influencing factor analysis.

Objective: Investigate the anxiety and depression states among dry eye (DE) patients during the COVID-19 outbreak and analyze their influence factors. Methods: The study was conducted in a tertiary eye hospital in Tianjin, China from March-April 2021. Four hundred twenty-eight DE patients were tested with the Ocular Surface Disease Index, Short Healthy Anxiety Inventory, Hospital Anxiety and Depression Scale, and Pittsburgh Sleep Quality Index. Descriptive statistics was used to assess the difference between DE with depression or anxiety among different groups. And multiple linear regression was used to explore factors that influence anxiety and depression in DE patients. Results: The incidence rates of anxiety and depression among DE patients during COVID-19 were 27.34 and 26.87%, respectively. The proportion with comorbid anxiety and depression was 24.30%. Patients' education level (t = -3.001, P < 0.05; t = -3.631, P < 0.05), course of disease (t = 2.341, P < 0.05; t = 2.444, P < 0.05), health anxiety (t = 3.015, P < 0.05; t = 2.731, P < 0.05), and subjective sleep quality (t = 3.610, P < 0.05; t = 4.203, P < 0.05) had certain influences on anxiety and depression. Conclusion: The results showed that subjective symptoms of DE patients were related to depression and anxiety. Higher education, shorter disease duration, lower health anxiety levels, and better subjective sleep quality were associated with the reduced depressive and anxiety symptoms in DE patients. These findings could be deemed beneficial to the treatment and prevention of DE during the COVID-19 epidemic.

Influence of Exchange Rate on Foreign Direct Investment Inflows: An Empirical Analysis Based on Co-Integration and Granger Causality Test

Although it is widely recognized that Foreign Direct Investment (FDI) inflows have a dominant effect on economic growth of host countries, the determinants of FDI inflows are still unclear. Especially, about the effect of exchange rate on FDI inflow, the results reached by scholars vary across countries or regions. It is of great practical and theoretical significance to explore the influencing effects of exchange rate on FDI inflow and identify the mechanisms that underlie them in close association with regional economic characters so as to help local government implement targeted government policies to achieve sustainable FDI inflow and sustainable economic growth. For this purpose, the influencing effects and the influencing mechanisms of the exchange rate on FDI inflows are investigated for Zhejiang province, China, over 1985–2019 by employing the co-integration tests, vector error correction models, Granger causality tests, and impulse response tests. Empirical results indicate that there are long-term stable and unidirectional causal relationship between the exchange rate and FDI inflow. Continuous appreciation of RMB against USD discourages FDI inflow. The mechanism which underlies the long-term relationship is the wealth effect, rather than the cost effect or the demand effect. By contrast, in the short run, neither the exchange rate nor the three influencing mechanism has a significant impact on FDI inflow. These results suggest policy recommendations for improving FDI by accumulating human capital and improving infrastructure. These findings are also applicable for other countries or regions with similar economic characters.

Clinical efficacy and adverse events of baricitinib treatment for coronavirus disease-2019 (COVID-19): A systematic review and meta-analysis.

The benefits of baricitinib in coronavirus disease-2019 are inadequately defined. We performed a systematic review and meta-analysis of studies of baricitinib to determine its clinical efficacy and adverse events in patients with COVID-19. Databases were searched from their inception to September 5, 2021. The primary outcome was the coefficient of mortality. We also compared secondary indicators and adverse events between baricitinib treatment and placebo or other treatments. Twelve studies of 3564 patients were included and assessed qualitatively (modified Jadad and Newcastle-Ottawa Scale scores). Baricitinib effectively improved the mortality rate (relative risk of mortality = 0.56; 95% confidence interval: 0.46-0.69; p < 0.001; I2 = 2%), and this result was unchanged by subgroup analysis. Baricitinib improved intensive care unit admission, the requirement for invasive mechanical ventilation, and improved the oxygenation index. Data from these studies also showed that baricitinib slightly reduced the risk of adverse events. Regarding the choice of the drug dosage of baricitinib, the high-dose group appeared to have additional benefits for clinical efficacy. Our study shows that baricitinib may be a promising, safe, and effective anti-severe acute respiratory syndrome-coronavirus-2 drug candidate, with the advantages of low cost, easy production, and convenient storage.

Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field.

We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.

The Efficacy of Computerized Cognitive Behavioral Therapy for Depressive and Anxiety Symptoms in Patients With COVID-19: Randomized Controlled Trial.

BACKGROUND: The prevalence of depressive and anxiety symptoms in patients with COVID-19 is higher than usual. Previous studies have shown that there are drug-to-drug interactions between antiretroviral drugs and antidepressants. Therefore, an effective and safe treatment method was needed. Cognitive behavioral therapy (CBT) is the first-line psychological therapy in clinical treatment. Computerized CBT (cCBT) was proven to be an effective alternative to CBT and does not require face-to-face therapy between a therapist and the patient, which suited the COVID-19 pandemic response. OBJECTIVE: This study aims to evaluate the efficacy of the cCBT program we developed in improving depressive and anxiety symptoms among patients with COVID-19. METHODS: We customized a cCBT program focused on improving depressive and anxiety symptoms among patients with COVID-19, and then, we assessed its effectiveness. Screening was based on symptoms of depression or anxiety for patients who scored ≥7 on the Hamilton Depression Rating Scale (HAMD17) or the Hamilton Anxiety Scale (HAMA). A total of 252 patients with COVID-19 at five sites were randomized into two groups: cCBT + treatment as usual (TAU; n=126) and TAU without cCBT (n=126). The cCBT + TAU group received the cCBT intervention program for 1 week. The primary efficacy measures were the HAMD17 and HAMA scores. The secondary outcome measures were the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Athens Insomnia Scale (AIS). Assessments were carried out pre- and postintervention. The patients' symptoms of anxiety and depression in one of the centers were assessed again within 1 month after the postintervention assessment. RESULTS: The cCBT + TAU group displayed a significantly decreased score on the HAMD17, HAMA, SDS, SAS, and AIS after the intervention compared to the TAU group (all P<.001). A mixed-effects repeated measures model revealed significant improvement in symptoms of depression (HAMD17 and SDS scores, both P<.001), anxiety (HAMA and SAS scores, both P<.001), and insomnia (AIS score, P=.002) during the postintervention and follow-up periods in the cCBT + TAU group. Additionally, the improvement of insomnia among females (P=.14) and those with middle school education (P=.48) in the cCBT + TAU group showed no significant differences when compared to the TAU group. CONCLUSIONS: The findings of this study suggest that the cCBT program we developed was an effective nonpharmacological treatment for symptoms of anxiety, depression, and insomnia among patients with COVID-19. Further research is warranted to investigate the long-term effects of cCBT for symptoms of anxiety, depression, and insomnia in patients with COVID-19. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000030084; http://www.chictr.org.cn/showprojen.aspx?proj=49952.