Platelet-to-lymphocyte ratio predicts the duration of glucocorticoid therapy in the treatment of cutaneous adverse drug reactions.

BACKGROUND: Glucocorticoid (GC) remains the mainstay of treatment for cutaneous adverse drug reactions (cADRs) but has been associated with side effects, emphasizing the importance of precisely managing the duration of high-dose GC treatment. Although the platelet-to-lymphocyte ratio (PLR) has been proven to be closely related to inflammatory disorders, its ability to predict the timing of GC dose reduction (Tr) during cADRs treatment remains obscure. METHODS: Hospitalized patients diagnosed with cADRs treated with glucocorticoids were analyzed in the present study to evaluate the association between PLR values and Tr values using linear, locally weighted scatter plot smoothing (LOWESS) and Poisson regression. Subgroup and ROC curve analyses were conducted to identify confounding variables and assess the predictive performance, respectively. RESULTS: A total of 308 patients were included in the study, with a median age of 47.0 (31.0-62.0) years old and a median incubation period of 4 days. Antibiotics (n = 113, 36.7%) were the most common cause of cADRs, followed by Chinese herbs (n = 76, 24.7%). PLR values were positively correlated with Tr values during linear regression (P < 0.001, r = 0.414) and LOWESS regression analyses. Poisson regression showed PLR was an independent risk factor for higher Tr values (the incidence rate ratio ranged from 1.016 to 1.070 and P < 0.05 for all). The area under the curve of PLR for predicting Tr < 7 days was 0.917. CONCLUSIONS: PLR is a simple and convenient parameter with huge prospects for application as a biomarker to assist clinicians in optimally managing patients treated with glucocorticoid therapy for cADRs.

Herpes Zoster Ophthalmicus Clinical Presentation and Risk Factors for Lesion Recovery.

Purpose: Herpes zoster ophthalmicus (HZO) causes trouble in patients' daily life and work. In severe cases, it may even lead to a decrease or loss of vision. To understand the demographic information and ocular symptoms of hospitalized patients with HZO, and to find potential factors related to improvement time of skin rash and duration of ocular symptoms at discharge, we design this study. Patients and Methods: This is a retrospective study. All patients diagnosed with HZO who were hospitalized in the Department of Dermatology of a hospital in Chongqing, China from January 1, 2015 to December 30, 2021 were included in this study. A total of 189 patients were included in this study. Clinical manifestations of the disease during hospitalization, improvement time of ocular skin lesions, and whether ocular skin lesions disappeared completely at discharge were recorded. Results: The most common ocular symptom was eyelid swelling (92.6%), followed by eye pain (48.7%). The most common ocular sign was conjunctivitis (78.3%), followed by keratitis (15.9%). There were 149 cases without residual ocular symptoms and 40 cases with residual ocular symptoms. There was no statistically significant difference in demographic characteristics between the two groups (P>0.05). Age ≥70 years (B=0.381, -0.061~0.022, P=0.005), use of glucocorticoids (B=0.260, 0.024~0.496, P=0.031), and use of topical antiviral drugs (B=0.380, 0.054~0.705, P=0.023) were factors affecting the time interval from admission to improvement of skin rash. Tearing (HR, OR=4.827, 1.956~11.909, P<0.001) and blood urea nitrogen (OR=0.787, 0.620-1.000, P=0.050) were factors influencing residual ocular symptoms. Conclusion: This study could help clinicians gain a deeper understanding of the clinical manifestations and partial influencing factors of HZO patients, which may contribute to future clinical work.

A "Square Box"-Structured Triboelectric Nanogenerator for Road Transportation Monitoring.

Nowadays, with the rapid development of e-commerce, the transportation of products has become more and more frequent. However, how to monitor the situation of products effectively and conveniently during road transportation is a long-standing problem. In order to meet this problem in practical applications, we fabricated a triboelectric nanogenerator sensor with a "square box" structure (S-TENG) for detecting the vibration suffered by vehicles. Specifically, with the spring installed in the S-TENG as a trigger, the two friction layers can contact and then separate to generate the real-time electrical signals when the S-TENG receives external excitation. The output voltage signals of the S-TENG under different vibration states were tested and the results demonstrated that the peak and zero positions of the open-circuit voltage-output curve are related to amplitude and frequency, respectively. In addition, the subsequent simulation results, obtained by ANSYS and COMSOL software, were highly consistent with the experimental results. Furthermore, we built a platform to simulate the scene of the car passing through speed bumps, and the difference in height and the number of speed bumps were significantly distinguished according to the output voltage signals. Therefore, the S-TENG has broad application prospects in road transportation.

Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression.

COVID-19 has swept through the world since December 2019 and caused a large number of patients and deaths. Spatial prediction on the spread of the epidemic is greatly important for disease control and management. In this study, we predicted the cumulative confirmed cases (CCCs) from Jan 17 to Mar 1, 2020, in mainland China at the city level, using machine learning algorithms, geographically weighted regression (GWR), and partial least squares regression (PLSR) based on population flow, geolocation, meteorological, and socioeconomic variables. The validation results showed that machine learning algorithms and GWR achieved good performances. These models could not effectively predict CCCs in Wuhan, the first city that reported COVID-19 cases in China, but performed well in other cities. Random Forest (RF) outperformed other methods with a CV-R 2 of 0.84. In this model, the population flow from Wuhan to other cities (WP) was the most important feature and the other features also made considerable contributions to the prediction accuracy. Compared with RF, GWR showed a slightly worse performance (CV-R 2 = 0.81) but required fewer spatial independent variables. This study explored the spatial prediction of the epidemic based on multisource spatial independent variables, providing references for the estimation of CCCs in the regions lacking accurate and timely.