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Head and neck squamous cell carcinoma (HNSCC) ranks as the sixth most common cancer among systemic malignant tumors, with 600 000 new cases occurring every year worldwide. Since HNSCC has high heterogeneity and complex pathogenesis, no effective prognostic indicator has yet been identified. Here, we aimed to identify a lncRNA signature associated with the prognosis of HNSCC as a potential new biomarker. LncRNA expression data were downloaded from The Cancer Genome Atlas database. A polygenic risk score model was constructed by using Lasso–Cox regression analysis. Weighted gene co‐expression network analysis (WGCNA) was applied to analyze the co‐expression modules of lncRNAs associated with the prognosis of HNSCC. The robustness of the signature was validated in testing and external cohorts. Polymerase chain reaction was performed to detect the expression levels of identified lncRNAs in cancer and adjacent tissues. We constructed an 8‐lncRNA signature (LINC00567, LINC00996, MTOR‐AS1, PRKG1‐AS1, RAB11B‐AS1, RPS6KA2‐AS1, SH3BP5‐AS1, ZNF451‐AS1) that could be used as an independent prognostic factor of HNSCC. The signature showed strong robustness and had stable prediction performance in different cohorts. WGCNA results showed that modules related to risk score mainly participated in biological processes such as blood vessel development, positive regulation of catabolic processes, and regulation of growth. The prognostic risk score model based on lncRNA for HNSCC may help clinicians conduct individualized treatment plans. I constructed an 8‐lncRNA signature (LINC00567, LINC00996, MTOR‐AS1, PRKG1‐AS1, RAB11B‐AS1, RPS6KA2‐AS1, SH3BP5‐AS1, ZNF451‐AS1) that may have potential as an independent prognostic factor of HNSCC. The signature showed strong robustness and good performance in different cohorts. My WGCNA results showed that modules related to risk score mainly participated in certain biological processes including blood vessel development and positive regulation of catabolic processes.

Background The health of migrants has received significant global attention, and it is a particularly significant concern in China, which has the largest migrant population in the world. Analyzing data on samples from the Chinese population holds practical significance. For instance, one can delve into an in-depth analysis of the factors impacting (1) the health records of residents in distinct regions and (2) the current state of family doctor contracts. This study explores the barriers to access these two health services and the variations in the effects and contribution magnitudes. Methods This study involved data from 138,755 individuals, extracted from the 2018 National Migration Population Health and Family Planning Dynamic Monitoring Survey database. The theoretical framework employed was the Anderson health service model. To investigate the features and determinants of basic public health service utilization among the migrant population across different regions of China, including the influence of enabling resources and demand factors, x 2 tests and binary logistic regression analyses were conducted. The Shapley value method was employed to assess the extent of influence of each factor. Results The utilization of various service types varied among the migrant population, with significant regional disparities. The results of the decomposition of the Shapley value method highlighted variations in the mechanism underlying the influence of propensity characteristics, enabling resources, and demand factors between the two health service types. Propensity characteristics and demand factors were found to be the primary dimensions with the highest explanatory power; among them, health education for chronic disease prevention and treatment was the most influential factor. Conclusion To better meet the health needs of the migrant population, regional barriers need to be broken down, and the relevance and effectiveness of publicity and education need to be improved. Additionally, by considering the education level, demographic characteristics, and mobility characteristics of the migrant population, along with the relevant health policies, the migrant population needs to be guided to maintain the health records of residents. They should also be encouraged to sign a contract with a family doctor in a more effective manner to promote the equalization of basic health services for the migrant population.

Objective This study investigates the coupling coordination relationships and influencing factors of health resources, health service utilization, and regional economy in China, aiming to provide reasonable suggestions for promoting the coordinated development of medical and health services and regional economy. Methods The panel data from 2016 to 2021 on China’s health resources, health service utilization were analyzed concerning China’s health resources, health service utilization, and regional economy. These included the entropy weight method for index weight calculation, the synthesis level index for subsystem synthesis level calculation, a coupled coordinated development model, exploratory spatial data analysis, and a geographic detector model. These analytical approaches facilitated the examination of spatial and temporal variances, alongside the identification of driving factors influencing the coupled and coordinated development of health resources, health service utilization, and regional economy in China. Results The findings reveal that (1) a high level of coupling exists among the three subsystems across all provinces in China. (2) Concerning the spatial pattern of coupling coordination, notable regional differences are observed in the degree of coupling coordination between health resource utilization, health service utilization, and regional economic development, with a noticeable decline from east to west and central regions positioned in between. (3) Regarding spatial evolution characteristics, an overall trend of higher levels is observed in the east and north compared to the west and south, indicating localized spatial clustering. In the south‒north direction, there is a parabolic rise followed by a decline from north to south. In the east‒west direction, there is an inverted “U” shaped curve with gradual strengthening from east to west. (4) The spatial disparities in synergetic development among health resource utilization, health service utilization, and the regional economy arise from multiple influencing factors interacting with each other. (5) Spatial variations are noted in the impacts of driving factors on coordinated development among health resource utilization, health service utilization, and the regional economy during different time periods. Conclusion Regional disparities primarily arise from various factors, such as healthcare visit frequency, healthcare human resource availability, and healthcare material resource allocation; moreover, these factors also vary geographically across different time periods. Strategic measures for medical care services and economic development should be formulated by each region based on its own circumstances to enhance economic benefits and achieve coordinated high-quality development among health resource utilization, health service utilization, and the regional economy.

In this study, an iron–zirconium binary oxide with a molar ratio of 4:1 was synthesized by a simple coprecipitation process for removal of phosphate from water. The effects of contact time, initial concentration of phosphate solution, temperature, pH of solution, and ionic strength on the efficiency of phosphate removal were investigated. The adsorption data fitted well to the Langmuir model with the maximum P adsorption capacity estimated of 24.9 mg P/g at pH 8.5 and 33.4 mg P/g at pH 5.5. The phosphate adsorption was pH dependent, decreasing with an increase in pH value. The presence of Cl − , SO 4 2− , and CO 3 2− had little adverse effect on phosphate removal. A desorbability of approximately 53 % was observed with 0.5 M NaOH, indicating a relatively strong bonding between the adsorbed PO 4 3− and the sorptive sites on the surface of the adsorbent. The phosphate uptake was mainly achieved through the replacement of surface hydroxyl groups by the phosphate species and formation of inner-sphere surface complexes at the water/oxide interface. Due to its relatively high adsorption capacity, high selectivity and low cost, this Fe–Zr binary oxide is a very promising candidate for the removal of phosphate ions from wastewater.

Autosomal dominant tubulointerstitial kidney disease (ADTKD) is a rare hereditary disorder characterized by slowly progressive loss of kidney function with absent or mild proteinuria and tubulointerstitial fibrosis. Mutations in the gene represent one of the primary genetic etiologies of ADTKD. This study presented the clinical data and genetic variant interpretation of three ADTKD patients diagnosed a next-generation sequencing (NGS)-based diagnostic workflow at the Department of Nephrology, Zhejiang Provincial People's Hospital, along with a review of relevant literature. Genetic analysis revealed three heterozygous UMOD variants: a novel p. Tyr559Cys, a variant previously recorded in databases (p. Ala500Val) but with limited clinical details, and a previously published variant (p. Leu66Pro) in a new family. This report contributes to expanding the mutation spectrum of and highlights its phenotypic variability, thereby enhancing clinical recognition and promoting timely diagnosis.

Acute kidney injury (AKI) is a frequent yet often overlooked complication. This study examines the incidence, unrecognized rate, and outcomes of AKI in adults at a large public Chinese hospital from 2010 to 2023. AKI rates were calculated, and outcomes were assessed using follow-up records. Multivariate logistic regression identified risk factors for unrecognized AKI. Among 2,790,540 patients, 5,080 met the AKI criteria, with an overall incidence of 0.18% (0.78% in hospitalizations, 0.05% in outpatients). The unrecognized AKI was 76.3%. In this group, 75% were stage 1, 16.7% stage 2, and 8.3% stage 3. Orthopedics had the highest unrecognized rate (94.5%) and ICUs the lowest (55.77%). Unrecognition of AKI improved from 90.3% in 2010–2011 to 70.2% in 2022–2023. AKI stage progression was linked to poorer survival. Patients with recognized AKI recovered faster than those with unrecognized AKI (8.0 vs. 9.0 days, p < 0.001). The mean follow-up time was 15.8 days, with similar rates at 28 and 90 days post-AKI for both groups. Risk factors for unrecognized AKI included lower AKI stage, baseline creatinine, absence of shock/heart disease/hypertension, and non-nephrology/surgery admissions. Non-nephrology physicians’ unfamiliarity with AKI guidelines may contribute to low awareness. Improved early detection and monitoring in high-risk groups are needed.

Acute kidney injury (AKI) is a concerned complication in patients with cancer receiving anti-PD-1/PD-L1 therapy, with severe AKI linked to adverse outcomes. Here, we constructed and validated a predictive model for severe AKI in these patients. Patients administered anti-PD-1/PD-L1 antibody at the Dongyang People’s Hospital from January 2019 to December 2023 (831patients) were included and randomly assigned into training and testing sets in a 7:3 ratio. An external validation dataset (907 patients) was obtained from Zhejiang Provincial People’s Hospital. Severe AKI was defined AKI stages 2 and 3, based on the kidney disease improving global outcomes criteria. Severe AKI occurred in 4.2% (73/1738) of patients: 5.3% (31/581), 5.2% (13/250), and 3.2% (29/907) in the training, testing, and external validation sets, respectively. Overall survival was significantly lower in patients with severe AKI. In the training set, a nomogram for severe AKI was constructed using three predictive factors: higher systolic blood pressure, lower serum albumin level, and diuretic use. In the training set, the model achieved C-indices of 0.850, 0.829, and 0.808 for predicting severe AKI at 90, 180, and 360 days, respectively. Corresponding C-indices in the test set were 0.710, 0.775, and 0.857, while those in the external validation cohort reached 0.720, 0.772, and 0.757, demonstrating strong discriminability. Calibration charts and decision curve analyses confirmed its calibration capability and clinical utility. The developed nomogram aids in predicting severe AKI risk in patients receiving anti-PD-1/PD-L1 antibodies and supports effective preventive interventions.

Background Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse events can interrupt treatment, affecting patient outcomes. Early prediction of AKI is essential for improved prognosis and personalized therapeutic strategies. Previous research has been constrained by significant limitations, underscoring the necessity for AKI risk prediction models for patients treated with PD-1/PD-L1 inhibitors. This study aimed to develop and validate an interpretable machine learning (ML) model for early AKI prediction in patients undergoing PD-1/PD-L1 inhibitor therapy using a retrospective cohort design. Methods This study collected data from patients treated with PD-1/PD-L1 inhibitors at Zhejiang Provincial People’s Hospital between January 2018 and January 2024. Nine ML models were evaluated. SHapley Additive exPlanations (SHAP) were employed to rank feature importance and interpret the final model. Additionally, a web-based calculator based on the model was developed. Results Among the nine ML models evaluated, the Grandient Boosting Machine (GBM) model achieved the best predictive performance. In the validation set, the GBM model achieved an AUC of 0.850 (95%CI: 0.830–0.870). In the test set, the AUC was 0.795(95% CI: 0.747–0.844), demonstrating accurate AKI risk prediction. Calibration curves demonstrated a strong concordance between predicted and observed risk probabilities. An interpretable final GBM model with 13 features was developed after feature reduction based on feature importance ranking. A web-based calculator accessible at https://predicatingaki.shinyapps.io/PDmodel/ has been developed to assist clinicians in AKI risk assessment. Conclusion This study developed and validated an interpretable ML model using a large dataset to predict AKI risk in patients receiving PD-1/PD-L1 inhibitor therapy. This model can assist clinicians in the early identification of high-risk patients, facilitating personalized treatment plans. Trial registration The study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of Zhejiang Provincial People’s Hospital (Approval No. KT2024116) in 3 Jan. 2025. As it was a retrospective study with anonymized data, informed consent was waived.

The development of nanofacula array is an effective methods to improve the performance of Near-field Scanning Optical Microscopy (NSOM) and achieve high-throughput array scanning. The nanofacula array is realized by preparing metal nanopore array through the \"two etching-one development\" method of double-layer resists and the negative lift-off process after metal film coating. The shading property of metal film plays important rules in nanofacula array fabrication. We investigate the shading coefficient of three kinds of metal films (gold–palladium alloy (Au/Pd), platinum (Pt), chromium (Cr)) under different coating times, and 3.5 min Au/Pd film is determined as the candidate of the nanofacula array fabrication for its lower thickness (about 23 nm) and higher shading coefficient (≥ 90%). The nanofacula array is obtained by irradiating with white light (central wavelength of 500 nm) through the metal nanopore array (250/450 nm pore diameter, 2 μm pore spacing and 7 μm group spacing). Moreover, the finite difference and time domain (FDTD) simulation proves that the combination of nanopore array and microlens array achieves high-energy focused nanofacula array, which shows a 3.2 times enhancement of electric field. It provides a new idea for NSOM to realize fast super-resolution focusing facula array.