Nanoscale Luminescence Imaging/Detection of Single Particles: State-of-the-Art and Future Prospects.

Single-particle-level measurements, during the reaction, avoid averaging effects that are inherent limitations of conventional ensemble strategies. It allows revealing structure-activity relationships beyond averaged properties by considering crucial particle-selective descriptors including structure/morphology dynamics, intrinsic heterogeneity, and dynamic fluctuations in reactivity (kinetics, mechanisms). In recent years, numerous luminescence (optical) techniques such as chemiluminescence (CL), electrochemiluminescence (ECL), and fluorescence (FL) microscopies have been emerging as dominant tools to achieve such measurements, owing to their diversified spectroscopy principles, noninvasive nature, higher sensitivity, and sufficient spatiotemporal resolution. Correspondingly, state-of-the-art methodologies and tools are being used for probing (real-time, operando, in situ) diverse applications of single particles in sensing, medicine, and catalysis. Herein, we provide a concise and comprehensive perspective on luminescence-based detection and imaging of single particles by putting special emphasis on their basic principles, mechanistic pathways, advances, challenges, and key applications. This Perspective focuses on the development of emission intensities and imaging based individual particle detection. Moreover, several key examples in the areas of sensing, motion, catalysis, energy, materials, and emerging trends in related areas are documented. We finally conclude with the opportunities and remaining challenges to stimulate further developments in this field.

Development of depression in patients using androgen deprivation therapy: A systemic review and meta-analysis.

BACKGROUND: Androgen deprivation therapy (ADT) is an effective treatment for advanced prostate cancer (PCa). Multiple studies have highlighted serious consequences this therapy poses to mental health, particularly depression. We aimed to review the incidence and association between ADT in men with PCa and the risk of depression. METHODS: We systematically searched multiple databases, including MEDLINE, Scopus till August 2023 for studies that compared ADT versus control for treating PCa reporting depression as outcome. Meta-analysis was performed using random-effects models and results presented as odds ratios (ORs) with 95% confidence interval (CI). Quality assessment of the included studies was conducted using Joanna Briggs Institute critical appraisal checklists. RESULTS: A total of 38 studies (17 retrospective studies, 16 prospective studies, two cross-sectional studies and two randomized trials) with 360,650 subjects met the inclusion criteria and were included in this meta-analysis. The estimated pooled incidence of depression among ADT patients is 209.5 (95% CI = 122.3; 312.2) per 1000 patients. There is statistically significant relationship between ADT treatment and depression (OR = 1.46, 95% CI = 1.28, 1.67; p = 0, I2 = 86.4%). The results remained consistent across various subgroups. No risk of publication bias was detected by funnel plot and Eggers's test (p > 0.05). CONCLUSION: There is a higher risk of depression for men receiving ADT. Further studies evaluating optimal treatments for depression in men on ADT are warranted.

Minimally Invasive Procedure versus Conventional Redo Sternotomy for Mitral Valve Surgery in Patients with Previous Cardiac Surgery: A Systematic Review and Meta-Analysis.

Background: The heightened morbidity and mortality associated with repeat cardiac surgery are well documented. Redo median sternotomy (MS) and minimally invasive valve surgery are options for patients with prior cardiac surgery who require mitral valve surgery (MVS). We conducted a systematic review and meta-analysis comparing the outcomes of redo MS and minimally invasive MVS (MIMVS) in this population. Methods: We searched PubMed, EMBASE, and Scopus for studies comparing outcomes of redo MS and MIMVS for MVS. To calculate risk ratios (RRs) for binary outcomes and weighted mean differences (MDs) for continuous data, we employed a random-effects model. Results: We included 12 retrospective observational studies, comprising 4157 participants (675 for MIMVS; 3482 for redo MS). Reductions in mortality (RR, 0.54; 95% confidence interval [CI], 0.37-0.80), length of hospital stay (MD, -4.23; 95% CI, -5.77 to -2.68), length of intensive care unit (ICU) stay (MD, -2.02; 95% CI, -3.17 to -0.88), and new-onset acute kidney injury (AKI) risk (odds ratio, 0.34; 95% CI, 0.19 to 0.61) were statistically significant and favored MIMVS (p<0.05). No significant differences were observed in aortic cross-clamp time, cardiopulmonary bypass time, or risk of perioperative stroke, new-onset atrial fibrillation, surgical site infection, or reoperation for bleeding (p>0.05). Conclusion: The current literature, which primarily consists of retrospective comparisons, underscores certain benefits of MIMVS over redo MS. These include decreased mortality, shorter hospital and ICU stays, and reduced AKI risk. Given the lack of high-quality evidence, prospective randomized control trials with adequate power are necessary to investigate long-term outcomes.

Multi-region electricity demand prediction with ensemble deep neural networks.

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.