Configuration-mediated excited-state energy dissipation in metal-bridged dimeric D-A fluorophores for enhanced photothermal therapy.

Photothermal agents (PTAs) based on donor (D)-acceptor (A) NIR fluorophores show great promise in photothermal therapy due to their accessible molecular engineering to mediate excitation energy for high photothermal conversion. Except for molecular structural modification of D-A fluorophores, intermolecular arrangement in space greatly influences their excitation energy dissipation as well. But how to mediate their intermolecular arrangement is still challenging. Here we control the intermolecular orientation of chromophores via metal coordination to form Pt-bridged dimeric D-A fluorophores with different geometries. The formed configuration isomers show different intermolecular exciton coupling behaviors involving charge transfer (CT) evolution and internally limited molecular rotation, which greatly affect excited-energy dissipation. Compared with folded configuration with intense NIR emission (quantum yields (QYs) = 15.62 %), linear configuration favors non-radiative decays with low QYs (6.99 %) but enhanced photothermal conversion efficiency (PCE = 41.57 %). The self-assembled nanoparticles combining Pt-bridged dimeric D-A fluorophores with DSPE-PEG2000-RGD reveal superior photothermal therapeutic features with desirable biosafety. This research provides a new designing concept to mediate excited-state energy dissipation pathways at a sub-nano level for enhanced photothermal conversion. STATEMENT OF SIGNIFICANCE: D-A fluorophores as photothermal agents attract great attention in photothermal therapy due to their accessible molecular engineering. Besides molecular engineering of D-A fluorophores, the intermolecular packing manner is proven to greatly affect their excitation energy dissipation. But how to control intermolecular arrangement is still challenging. Here we control the intermolecular orientation of chromophores via metal coordination to form Pt-bridged dimeric D-A fluorophores with different geometries. Compared to the folded configuration, linear configuration facilitates charge transfer (CT) evolution and molecular rotation, which promotes non-radiative decays of excited energy for enhanced photothermal therapy.

Development and Validation of a Nomogram Based on Geriatric Nutritional Risk Index to Predict Surgical Site Infection Among Gynecologic Oncology Patients.

Background: The geriatric nutritional risk index (GNRI) is a commonly used method to assess nutritional risk for predicting potential surgical site infections (SSI) in cancer patients. This study aims to create and verify a simple nomogram and a dynamic web-based calculator for predicting the risk of SSI among gynecologic oncology patients. Methods: A retrospective evaluation was conducted on patients who were admitted into a tertiary hospital in China with confirmed diagnosis of gynecologic cancer between 01 August 2017 and 30 November 2021. A two-piecewise linear regression model with a smoothing function was used to investigate the non-linear association between GNRI and SSI to determine the ideal cut-off point. Three models were developed on the basis of different variables to predict SSI in gynecologic oncology patients. Through a nomogram the concordance index (C-index), the Akaike information criterion (AIC), and the integrated discrimination index (IDI) were used to determine the final model. Finally, the performance of the nomogram was validated using the 1,000-bootstrap resamples method and analyzed using C-index, GiViTI calibration belts, and decision curve. Also, a user-friendly dynamic web-based calculator was developed. Results: A total of 1,221 patients were included in the analysis. A non-linear association could be observed between GNRI and SSI risk with a GNRI cut-off value of 101.7. After adding GNRI to Model 2 (which comprised Morse Fall Scale score, preoperative length of stay, operation time, and estimated blood loss), the AIC value decreased, the C-index value increased and IDI increased significantly. The nomogram C-index in the development cohort and internal validation cohort demonstrates a moderate-high degree of discrimination. The GiViTI calibrated belt showed a good agreement between the observed and predicted probabilities of SSI. The decision curve validates the clinical feasibility of the nomogram with a threshold value between 0 and 49%. Conclusion: The GNRI cut-off value of 101.7 allowed for appropriate stratification of patients into distinct SSI risk groups. This study found that including GNRI in the above nomogram (Model 2) would enhance its potential to predict SSI in gynecologic oncology patients.

Derivation and validation of a nomogram for predicting nonventilator hospital-acquired pneumonia among older hospitalized patients.

BACKGROUND: Currently, there is no effective tool for predicting the risk of nonventilator hospital-acquired pneumonia (NV-HAP) in older hospitalized patients. The current study aimed to develop and validate a simple nomogram and a dynamic web-based calculator for predicting the risk of NV-HAP among older hospitalized patients. METHODS: A retrospective evaluation was conducted on 15,420 consecutive older hospitalized patients admitted to a tertiary hospital in China between September 2017 and June 2020. The patients were randomly divided into training (n = 10,796) and validation (n = 4624) cohorts at a ratio of 7:3. Predictors of NV-HAP were screened using the least absolute shrinkage and selection operator method and multivariate logistic regression. The identified predictors were integrated to construct a nomogram using R software. Furthermore, the optimum cut-off value for the clinical application of the model was calculated using the Youden index. The concordance index (C-index), GiViTI calibration belts, and decision curve were analysed to validate the discrimination, calibration, and clinical utility of the model, respectively. Finally, a dynamic web-based calculator was developed to facilitate utilization of the nomogram. RESULTS: Predictors included in the nomogram were the Charlson comorbidity index, NRS-2002, enteral tube feeding, Barthel Index, use of sedatives, use of NSAIDs, use of inhaled steroids, and "time at risk". The C-index of the nomogram for the training and validation cohorts was 0.813 and 0.821, respectively. The 95% CI region of the GiViTI calibration belt in the training (P = 0.694) and validation (P = 0.614) cohorts did not cross the diagonal bisector line, suggesting that the prediction model had good discrimination and calibration. Furthermore, the optimal cut-off values for the training and validation cohorts were 1.58 and 1.74%, respectively. Analysis of the decision curve showed that the nomogram had good clinical value when the threshold likelihood was between 0 and 49%. CONCLUSION: The developed nomogram can be used to predict the risk of NV-HAP among older hospitalized patients. It can, therefore, help healthcare providers initiate targeted medical interventions in a timely manner for high-risk groups.