Objective To explore the prevalence of depressive symptoms in postoperative patients with ovarian cancer and to analyze its influencing factors from multiple dimensions, including clinical characteristics, psychological factors, and laboratory indicators. Methods A cross-sectional study was conducted, which enrolled 235 postoperative patients with ovarian cancer. Depressive status was assessed using the patient health questionnaire, and the demographic, pathological, and medical record data of the patients were collected using the generalized anxiety disorder scale, Pittsburgh sleep quality index, European organization for research and treatment of cancer quality of life questionnaire core 30, and ECOG performance status score. Peripheral blood tumor marker (CA125), routine blood test, lymphocyte subsets, and serum cytokine levels were measured. Univariate and multivariate binary logistic regression analysis were used for statistical analysis. Results The prevalence of depression in postoperative patients with ovarian cancer was 39.15% (92/235). Univariate analysis showed that ECOG score ≥ 2 points, pain, anxiety, poor sleep quality, low quality of life, low life satisfaction, tumor recurrence, six or more cycles of chemotherapy, as well as higher levels of CA125, NLR, and NAR, and lower hemoglobin levels were significantly associated with depression (all P<0.05). Multivariate binary Logistic regression analysis showed that anxiety (OR=1.975, 95%CI: 1.231-3.170), sleep efficiency (OR=4.181, 95%CI: 1.211-14.43), sleep latency (OR=34.806, 95%CI: 4.258-284.542), ECOG performance status score, cognitive function (OR=0.918, 95%CI: 0.868-0.97), and life satisfaction were independent risk factors for depression (all P<0.05). Laboratory indicators were not independent influencing factors in the multivariate Logistic regression model. Conclusion Depression in postoperative patients with ovarian cancer is influenced by physiological, psychological, and social factors. Clinical management should focus on patients with anxiety, sleep disorders, poor physical condition, and low life satisfaction, and a comprehensive prevention and treatment strategy centered on psychological intervention and taking into account symptom management and social support should be implemented.

Plant-derived extracellular vesicles (PDEVs) are nanoscale extracellular vesicles secreted by plant cells, characterized by a lipid bilayer structure. These vesicles carry a variety of bioactive molecules, including proteins, nucleic acids, and lipids, and play essential roles in intercellular communication and physiological regulation in plants. Compared to animal-derived extracellular vesicles, PDEVs offer several advantages, such as a broad range of sources, high biocompatibility, low immunogenicity, and low production costs. Furthermore, PDEVs have demonstrated remarkable potential as natural nanocarriers for drug delivery, due to their ability to efficiently traverse biological barriers, such as the blood-brain barrier, making them promising candidates for drug delivery systems. This review systematically elaborates on the complex composition of PDEVs, which consists of lipids, proteins, and nucleic acids, the typical structural characteristics of their lipid bilayers ranging from 30 to 150 nm, and their versatile loading capabilities as drug carriers, efficiently encapsulating various types of therapeutic agents such as hydrophilic small molecules, hydrophobic drugs, nucleic acids, and proteins. We systematically summarize the recent advancements in strategies for enhancing the loading efficiency of PDEVs, which include methods such as co-incubation, ultrasound-assisted loading, electroporation, freeze-thaw cycles, and microfluidic technology. These techniques are evaluated based on their underlying principles, suitable drug types, and their respective advantages. In addition to loading strategies, we focus on the engineered approaches to achieve targeted delivery using PDEVs, such as genetic engineering modifications, chemical ligand conjugation, membrane fusion technology, and polyethylene glycol (PEG) modification. We discuss the mechanisms of these strategies in enhancing targeting efficiency, prolonging in vivo circulation time, and improving therapeutic efficacy. Further, this review highlights the application of PDEVs in various disease models, including tumor, skin inflammation, metabolic disorders, and neurodegenerative diseases, showcasing their therapeutic potential as multifunctional delivery platforms. The ability of PDEVs to encapsulate diverse therapeutic agents and target specific tissues or cells opens up new avenues for the treatment of complex diseases, offering advantages over conventional drug delivery systems. However, despite the promising applications of PDEVs, several challenges remain in their development and clinical translation. These challenges include variability in source materials, standardization of preparation processes, quality control, scalability of production, and the need for clinical validation. To overcome these obstacles, the integration of advanced technologies such as artificial intelligence-assisted design and multi-omics analysis is proposed as a way to facilitate the precise development of PDEVs. These emerging technologies hold the potential to further enhance the precision and effectiveness of plant-based drug delivery systems, ultimately advancing the field of precision medicine. In conclusion, the use of PDEVs as a platform for drug delivery represents a promising area of research with the potential to revolutionize therapeutic strategies. Their ability to encapsulate and deliver a wide variety of bioactive molecules, along with their inherent advantages in biocompatibility and versatility, makes them a valuable tool in the development of more efficient and targeted therapeutic interventions. Continued research and innovation in this field will pave the way for the clinical implementation of PDEVs in the treatment of various diseases, offering new hope for more effective and sustainable therapeutic options.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

ObjectiveTo understand the infection characteristics of multidrug-resistant organisms (MDROs) in hospitalized patients in a tertiary sentinel hospital in Shanghai, so as to provide an evidence for the development of targeted prevention and control measures. MethodsData of MDROs strains and corresponding medical records of some hospitalized patients in a hospital in Shanghai from 2021 to 2023 were collected, together with an analysis of the basic information, clinical treatment, underlying diseases and sources of sample collection. ResultsA total of 134 strains of MDROs isolated from hospitalized patients in this hospital were collected from 2021 to 2023 , including 63 strains of methicillin-resistant Staphylococcus aureus (MRSA), 57 strains of carbapenem-resistant Acinetobacter baumannii (CRAB), and 14 strains of carbapenem-resistant Klebsiella pneumoniae (CRKP). Of the 134 strains, 30 strains were found in 2021, 47 strains in 2022 and 57 strains in 2023. The male-to-female ratio of patients was 2.05∶1, with the highest percentage (70.90%) in the age group of 60‒<90 years. The primary diagnosis was mainly respiratory disease, with lung and respiratory tract as the cheif infection sites. There was no statistically significant difference in the distribution of strains between different genders and infection sites (P>0.05). However, the differences in the distribution of strains between different ages and primary diagnosis were statistically significant (P<0.05). Patients who were admitted to the intensive care unit (ICU), had urinary tract intubation, were not artery or vein intubated, were not on a ventilator, were not using immunosuppresants or hormones, and were not applying radiotherapy or chemotherapy were in the majority. There was no statistically significant difference in the distribution of strains for whether received radiotherapy or chemotherapy or not (P>0.05), while the differences in the distribution of strains with ICU admission history, urinary tract intubation, artery or vein intubation, ventilator use, and immunosuppresants or hormones use or not were statistically significant (all P<0.05). The type of specimen was mainly sputum, the hospitalized ward was mainly comprehensive ICU, the sampling time was mainly in the first quarter throughout the year, the number of underlying diseases was mainly between 1 to 2 kinds, the application of antibiotics ≥4 kinds, and those who didn’t receive any surgery recently accounted for the most. There were statistically significant differences in the distribution of strains between different specimen types, wards occupied and history of ICU stay (P<0.05), but no statistically significant difference in the distribution of strains between different sampling times, number of underlying diseases and types of antibiotics applied (P>0.05). ConclusionThe situation of prevention and control on MDROs in this hospital is still serious. Focus should be placed on high-risk factors’ and infection monitoring and preventive measures should be strengthened to reduce the incidence rate of MDROs infection.

Objective To explore the influence of radiation protection knowledge-attitude-practice (RP-KAP) on occupational stress of radiation workers. Methods A total of 314 radiation workers from five hospitals in Shijiazhuang City were selected as the study subjects using the convenient sampling method. The Chinese version of the "Effort-Reward Imbalance (ERI) Questionnaire" and the "Radiation Protection Knowledge, Attitude, and Practice Questionnaire" were used for investigation. Results The detection rate of occupational stress in ERI model among the radiation workers was 74.5% (234/314). The RP-KAP practice dimension score of the population in the occupational stress group was lower than that in the non-occupational stress group (P<0.05). The results of binary logistic regression analysis showed that radiation workers with lower RP-KAP practice dimension score had a higher risk of occupational stress (P<0.01), and the risks of occupational stress among population of interventional radiology group and radiotherapy group were higher than that of X-ray diagnosis group and nuclear medicine group (both P<0.05), after controlling for confounding factors such as gender, age, type of work, professional title, daily working hours, weekly working hours and regular vacation. Conclusion RP-KAP is the influencing factor of occupational stress in the radiation workers. To improve the radiation workers' knowledge of radiation protection, protection awareness and compliance with protective behavior can effectively reduce or even eliminate occupational stress.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

ObjectiveThis nested case-control study, based on the Shanghai Birth Cohort (SBC), aimed to explore the impact of early pregnancy exposure to organophosphorus flame retardants (OPFRs) on attention deficit hyperactive disorder (ADHD)-like symptoms in 4-year-old children, so as to provide epidemiological evidence regarding the health effects of emerging contaminant OPFRs in children. MethodsStrengths and Difficulties Questionnaire (SDQ) was used to assess ADHD like symptoms in 4-year-old children. Children with an SDQ hyperactivity subscale score ≥6 points were defined as cases, while those with a score <5 points were considered as controls. The case and control groups were matched at 1∶1 based on the child’s age (±6 months), sex, and parental or primary caregiver’s education level. A total of 105 cases and 112 controls were included eventually. Concentrations of eight OPFRs metabolites in early pregnancy urine samples were measured using ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), including di-phenyl phosphate (DPHP), di-m-cresylphosphate (DmCP), di-o-cresylphosphate (DoCP), di-p-cresylphosphate (DpCP), di-n-butyl phosphate (DnBP), di-iso-butyl phosphate (DiBP), bis(2-butoxyethyl) phosphate (BBOEP), and bis(2-ethylhexyl) phosphate (BEHP). Basic demographic information of mothers and children were collected through questionnaire surveys and medical records extraction. Binary logistic regression models were used to analyze the effect of individual OPFRs exposure during early pregnancy on ADHD-like symptoms, while a quantile g-computation (Qgcomp) regression model was employed to assess the effects of mixed OPFRs exposure (with detection rates >75%) on ADHD-like symptoms in 4-year-old children. ResultsIn this study, the detection rates of DPHP, DoCP, and the DmCP&DpCP in the urine of early pregnancy women were higher than 75%, with DPHP having the highest detection rate (86.18%). The median concentrations of DPHP were highest in both the case and control groups (0.396 μg·L^-1 and 0.305 μg·L^-1, respectively). Binary logistic regression analyses revealed that exposure to DPHP during early pregnancy increased the risk of ADHD-like symptoms in 4-year-old children (OR=1.262, 95%CI: 1.017‒1.565). The mixed exposure model analyses showed that early pregnancy co-exposure to OPFRs increased the risk of ADHD-like symptoms (OR=1.508, 95%CI: 1.012‒2.258), with DPHP being the primary contributor to the association. ConclusionEarly pregnancy exposure to DPHP is positively associated with an increased risk of ADHD-like symptoms in 4-year-old children. Additionally, DPHP contributed the most to the adverse effects of mixed OPFRs exposure on ADHD-like symptoms. However, these findings require further validation through other large-scale prospective cohort studies.

Objective:To establish ultra-performance liquid chromatography (UPLC) fingerprint chromatograms for Erigeron breviscapu from different origins and storage conditions; To identify the Erigeron breviscapus from different habitats by chemometric analysis.Methods:Acquity UPLC HSS T3 (2.1 mm×100 mm,1.8 μm) chromatography column was used; mobile phase was 0.1% formic acid aqueous solution-acetonitrile; detection wavelength was 268 nm; flow rate was 0.25 ml/min; column temperature was 35 ℃; injection volume was 2 μl gradient elution. The UPLC fingerprint of Erigeron breviscapu from different origins was established. Similarity evaluation combined with chemometric analysis methods such as clustering analysis (CA), principal component analysis (PCA), orthogonal partial least squares method - discriminant analysis (OPLS-DA) were used to identify the origin of Breviscapine from different places. Fingerprint technology was used to evaluate the similarity of storage conditions for Erigeron breviscapu.Results:The UPLC fingerprint method met the methodological requirements. 30 batches of Erigeron breviscapus had 24 common peaks, and the similarity between Xingyi in Guizhou and Huize in Qujing was 0.702-0.783, while the similarity between Honghe Luli, Kunming Fuming and Dali was 0.861-0.970. All samples were divided into two categories according to their origin by CA: category Ⅰ: Xingyi in Guizhou and Huize in Qujing, and category Ⅱ: Dali, Kunming Fuming and Honghe Luli. The results of PCA were consistent with CA. OPLS-DA screened out 10 differential markers of Erigeron breviscapus from different habitats. Moreover, a total of 40 common peaks were identified in six batches of Erigeron breviscapus samples stored under different conditions. Based on the similarity analysis, Erigeron breviscapus samples stored at 30% humidity and 70% humidity were classified into two separate categories.Conclusions:The fingerprint method constructed in this study is stable and reliable, and the predictive ability and reliability of the OPLS-DA model are excellent. By combining the two methods, a clear division can be made between Erigeron breviscapus from Yunnan and Guizhou. Humidity conditions are an important factor in storing Erigeron breviscapus.

Background Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental and behavioral disorder in children, often diagnosed during school age. The etiology of ADHD remains unclear; however, existing studies suggest that environmental factors, such as exposure to triclosan (TCS), may be associated with the occurrence of ADHD-like symptoms in offspring. Nevertheless, relevant research in China remains limited. Objective To investigate the impact of early pregnancy TCS exposure on ADHD-like symptoms in 7-year-old children. Methods This study was based on the Shanghai Birth Cohort (SBC) and included 662 mother-child pairs. TCS concentrations in early pregnancy urine samples were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Demographic information was collected via questionnaires and medical record abstraction. ADHD-like symptoms in 7-year-old children were first assessed using the Strengths and Difficulties Questionnaire (SDQ). Further differentiation of ADHD-like symptom subtypes (inattentive and hyperactive/impulsive) was conducted using the SNAP-IV, a clinically validated ADHD screening tool. Negative binomial regression models were applied to evaluate the associations between prenatal TCS exposure and hyperactive behavior (SDQ assessment) as well as ADHD-like symptom subtypes (SNAP-IV assessment) in 7-year-old children. Results The positive rate of TCS in early pregnancy urine samples was 91.39%, with median concentrations of 0.69 μg·L⁻¹ and 0.63 μg·g⁻¹ before and after the creatinine adjustment, respectively. The modeling results indicated that prenatal TCS exposure was associated with an increased risk of hyperactive symptoms (SDQ assessment) in 7-year-old children (RR=1.04, 95%CI: 1.02, 1.06); the stratified analyses by children sex revealed similar effects for both boys (RR=1.04, 95%CI: 1.02, 1.07) and girls (RR=1.04, 95%CI: 1.01, 1.07). Further analysis of ADHD-like symptom subtypes showed that prenatal TCS exposure increased the risk of inattentive symptoms (RR=1.03, 95%CI: 1.00, 1.05); the sex-stratified analyses indicated associations between TCS exposure and inattentive symptoms (RR=1.03, 95%CI: 1.00, 1.07) as well as hyperactive/impulsive symptoms (RR=1.04, 95%CI: 1.01, 1.08) in girls. Conclusion Prenatal TCS exposure is associated with an increased risk of ADHD-like symptoms in 7-year-old children, primarily contributing to the risk of the inattention subtype. The impact is more pronounced in girls.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.