With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Drug administration via hollow microneedles (HMN) have the advantages of painlessness, avoidance of first-pass effect, capability of sustained infusion, and no need for professional personnel operation. In addition, HMN can also be applied in the fields of body fluid extraction and biosensors, showing broad application prospects. However, traditional manufacturing technologies cannot meet the demand for low-cost mass production of HMN, limiting its widespread application. This paper reviews the main manufacturing technologies used for HMN in recent years, which include photolithography and etching, laser etching, sputtering and electroplating, micro-molding, three-dimensional (3D) printing and drawing lithography. It further analyzes the characteristics and limitations of existing manufacturing technologies and points out that the combination of various manufacturing technologies can improve production efficiency to a certain extent. In addition, this paper looks forward to the future trends of HMN manufacturing technology and proposes possible directions for its development. In conclusion, it is expected that this review can provide new ideas and references for follow-up research.
Printing, Three-Dimensional ; Needles ; Humans ; Drug Delivery Systems/methods* ; Equipment Design ; Microinjections/methods*

Objective: To investigate the association of sleep and circadian rhythm disruption indicators (including chronotype, sleep duration, and social jetlag) with co-occurring depressive and anxiety symptoms among primary and secondary school students, so as to provide a reference for promoting their mental health. Methods: In October 2023, a total of 15 944 primary and secondary school students were recruited from Nanjing, using a stratified cluster random sampling method. The Morning and Evening Questionnaire-5, Center for Epidemiological Studies Depression, and Generalized Anxiety Disorder-7 were used for the survey. Chi-square test was employed for intergroup comparisons, and Logistic regression model was applied to analyze the independent and joint effects of sleep related factors on comorbid symptoms of depressive and anxiety among primary and middle school students. Results: The prevalence of co-occurring depressive and anxiety symptoms among primary and secondary school students in Nanjing was 16.9%. After adjusting for covariates, Logistic regression analysis revealed significant independent associations between evening chronotype ( OR=6.55, 95%CI =5.59-7.68), insufficient sleep duration ( OR=3.05, 95%CI =2.60-3.59), and social jetlag ≥2 h ( OR= 2.09 , 95%CI =1.85-2.37) with comorbid symptoms of depressive and anxiety among students (all P <0.05). Concurrent of evening chronotype and insufficient sleep ( OR=7.54, 95%CI =3.55-16.01), as well as evening chronotype and social jetlag ≥2 h ( OR=4.18, 95%CI =3.01-5.81), were associated with an increased risk of co-occurring depressive and anxiety symptoms (both P < 0.05 ). In the female and high school student subgroups, the combination of evening chronotype and insufficient sleep or social jetlag ≥2 h showed stronger joint effects on co-occurring depressive and anxiety symptoms [ OR (95% CI )=8.46(3.25-22.04) and 15.90(3.66-69.08); 7.87(4.90-12.65) and 4.85(3.10-7.59), respectively; all P <0.05]. Conclusions Evening chronotype, insufficient sleep, and social jetlag≥2 h may serve as risk factors for comorbid symptoms of depressive and anxiety in school aged populations. Paying attention to the coexistence of multiple sleep related risk factors may help mitigate the occurrence of emotional disorders in this demographic.

Objective To explore the feasibility of long non-coding RNAs (lncRNAs) as biomarkers for radiation-induced intestinal injury. Methods Mice were exposed to 15 Gy of ⁶⁰Co γ-rays to the abdominal area. The pathological changes in intestinal tissues were analyzed at 72 h post-irradiation to confirm the successful establishment of the radiation-induced intestinal injury model. Real-time quantitative PCR was conducted to detect the expression of candidate radiation-responsive lncRNAs in the jejunum, jejunal crypts, colon tissues, and plasma of irradiated mice. Human intestinal epithelial cell line HIEC-6 and human colon epithelial cell line NCM460 were exposed to 0, 5, 10, and 15 Gy of ⁶⁰Co γ-rays. The expression levels of candidate lncRNAs were measured at 4, 24, 48, and 72 h post-irradiation to observe their changes with the irradiation dose. Results Pathological analysis showed that abdominal irradiation with 15 Gy successfully established an acute radiation-induced intestinal injury mouse model. Real-time quantitative PCR showed that Dino, Lncpint, Meg3, Dnm3os, Trp53cor1, Pvt1, and Neat1 were significantly upregulated following the occurrence of radiation-induced intestinal injury (P < 0.05). Among them, Meg3 and Dnm3os in mouse plasma were significantly upregulated (P < 0.05), while Gas5 was significantly downregulated (P < 0.05). In HIEC-6 and NCM460 cells, the expression levels of DINO, MEG3, DNM3OS, and GAS5 showed dose-dependent patterns at certain time points (P < 0.05). Conclusion The lncRNAs encoded by MEG3, DNM3OS, and GAS5 in intestinal epithelial cells are responsive to ionizing radiation. Consistent differential expression changes were detected in mouse plasma and intestinal tissues, indicating their potential as biomarkers for radiation-induced intestinal injury.

Objective:To evaluate auditory spatial discrimination capabilities in patients with mild to moderately severe symmetrical sensorineural hearing loss (SNHL) and to compare the impact of different psychophysical testing methods on Minimum Audible Angle (MAA) and test duration.Methods:A total of 105 symmetrical SNHL patients aged from 18 to 60 years old were enrolled from April to July 2023, including 56 males and 49 females. They were divided into three groups based on PTA: mild, moderate, and moderately severe hearing loss, with 35 individuals in each group. Additionally, a control group of 35 individuals with normal hearing was tested, including 18 males and 17 females. Participants underwent four distinct psychophysical discrimination tests: the block up-down, 1-up/1-down, 1-up/2-down, and 1-up/3-down procedures. We recorded the MAA and test duration for each. We employed repeated measures of ANOVA to compare the MAA and test duration across different methods and groups, and Pearson′s correlation to assess the relationship between MAA and degree of hearing loss.Results:MAA of sound localization in patients with symmetrical SNHL was significantly positively correlated with the degree of hearing loss ( r=0.59, P<0.01). Significant deterioration in MAA was observed as hearing loss progressed to the moderate level (PTA≥35 dBHL, P<0.01). The testing methods significantly influenced MAA and testing duration ( F=24.02, P<0.01; F=75.56, P<0.01) and the 1-up/1-down method was the quickest, averaging only (0.69±0.32) mins. Conclusions:The horizontal plane auditory spatial discrimination abilities in patients with symmetrical SNHL is impaired progressively with increasing hearing loss, notably beyond moderate hearing loss levels. Different psychophysical methods influence both MAA and test duration, the quicker 1-up/1-down method is recommended for assessing MAA in symmetrical SNHL patients.

Objective: To explore the relationship between lifestyle and myopia and depressive symptoms comorbidity among primary and secondary school students in Nanjing, so as to provide a scientific basis for developing effective prevention measures. Methods: In October 2022, a stratified cluster random sampling method was used to select primary and secondary school students aged 9-19 years in 4 urban and 4 suburban districts in Nanjing as the research subjects. A total of 10 498 students were included for physical examination and questionnaire survey by using the student health condition and influencing factors questionnaire. Chisquare test was used for univariate analysis, and multifactor Logistic regression analysis was used to analyze the relationship between students lifestyle and the cooccurrence of myopia and depressive symptoms. Results: The prevalence of comorbidity of screening positive myopia and depression among primary and secondary school students in Nanjing was 18.11%. The prevalence of comorbidity was higher in girls (20.97%) than in boys (15.47%), higher in boarding students (31.31%) than in nonboarding students (16.51%), and higher in high school students than in middle and primary school students (28.63%, 19.10%, 7.76%), with statistically significant differences (χ2=53.49, 149.31, 522.55, P<0.01). Multivariate Logistic regression results showed that smoking (OR=1.51), drinking (OR=2.36), looking at electronic screens in dim conditions (OR=2.40), screen time ≥2 h/d(OR=1.50), afterschool homework time ≥2 h/d(OR=1.48) were positively correlated with the prevalence of comorbidity of screening positive myopia and depressive symptoms, healthy diet (OR=0.67), outdoor activities during breaks (OR=0.80), sufficient sleep (OR=0.64), meeting physical activity standards (OR=0.74) and outdoor activity time ≥2 h/d(OR=0.84) were negatively correlated with the prevalence of comorbidity of screening positive of myopia and depressive symptoms (P<0.05). Conclusions The lifestyle of primary and secondary school students in Nanjing is related to the comorbidity of myopia and depressive symptoms. Schools and families should carry out relevant education and intervention measures to promote students to develop good living habits and jointly prevent the occurrence of myopia and depressive symptoms.

Purpose To explore the feasibility of the deep learning-based segmentation of extra-pelvic region and metastases in advanced prostate cancer based on metastasis reporting and data system for prostate cancer(MET-RADS-P).Materials and Methods Four datasets(68,91,57 and 263 patients with head,neck,chest and abdomen metastases,respectively)from Jan 2017 to Jan 2022 in Peking University First Hospital were retrospectively collected for the development of the classification model of scanning range and segmentation model of different regions and metastases according to the scanning sites(head,neck,chest and abdomen).In addition,90 patients with prostate cancer confirmed by pathology and underwent whole-body MRI were collected for external validation of the developed model.The manual annotation of the regions and metastases were used as the"reference standard"for the model evaluation.The evaluation indexes included dice similarity coefficient(DSC)and volumetric similarity(VS).Results In the external validation set,the classification accuracy of head,neck,chest and abdomen were 100%(90/90),98.89%(89/90),96.67%(87/90)and 94.44%(85/90),respectively.The range of DSC,VS values of the segmentation model for organs in different regions were(0.86±0.10)-(0.99±0.01),(0.89±0.10)-(0.99±0.01),respectively.The range of DSC,VS values of the segmentation model for metastases in different regions were(0.65±0.07)-(0.72±0.13),(0.74±0.04)-(0.82±0.13),respectively.Conclusion The 3D U-Net model based on deep learning may achieve the segmentation of extra-pelvic region and metastasis in advanced prostate cancer.