Background Exposure to air pollutants increases the risk of diseases in multiple systems, including respiratory and cardiovascular systems, yet its association with neurological diseases remains unclear. Objective To quantitatively evaluate the association between short-term exposure to air pollutants and outpatient and emergency visits for neurological diseases, identify potential susceptible populations, and quantify associated disease burden. Methods Daily 24-hour average concentrations of fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO), daily maximum 8-hour average concentration of ozone (O₃), daily meteorological data (24-hour average temperature, 24-hour average relative humidity), and data on daily outpatient and emergency department visits for neurological diseases from two hospitals in Conghua District, Guangzhou, China, were collected from 2015 to 2022. A time-stratified case-crossover design was adopted, and a conditional Poisson regression model was constructed to analyze the association between air pollution exposure and neurological disease visits. Two-pollutant models and sensitivity analysis were used to validate model stability. Stratified analyses by season (cold season: from November to March; warm season: from April to October), sex (male, female), and age (≤45 years, 46–60 years, ≥61 years) were performed to identify vulnerable group. Additionally, the number and proportion of neurological disease visits attributable to short-term air pollutant exposure were calculated. Results A total of 72 673 outpatient and emergency department visits for neurological diseases were included during the study period. Most of the patients were middle-aged and elderly individuals (69.89% were over 45 years old) and females (60.25%). The results of single-pollutant models showed that for each interquartile range (IQR) increase in exposure to PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃, the risk of outpatient and emergency department visits for neurological diseases increased by 7.54% (95%CI: 4.69%, 10.46%), 6.66% (95%CI: 3.92%, 9.46%), 16.72% (95%CI: 10.58%, 23.19%), 8.12% (95%CI: 4.82%, 11.53%), 5.60% (95%CI: 2.34%, 8.97%), and 6.11% (95%CI: 2.91%, 9.40%), respectively. The results of the two-pollutant model showed that the association between PM_2.5 and SO₂ exposure and outpatient and emergency department visits for neurological diseases were relatively stable. The stratified analyses showed that the effect of SO₂ was stronger in the cold season. It was estimated that 8.32% (95%CI: 5.55%, 10.96%) and 6.65% (95%CI: 4.27%, 8.96%) of the outpatient and emergency department visits were attributable to short-term exposure to SO₂ and PM_2.5, respectively. Conclusion Exposure to PM_2.5 and SO₂ is associated with increased risks of outpatient and emergency visits for neurological diseases. SO₂ shows stronger effects during the cold season, and exposure to air pollution contributes to up to 8.32% of neurological disease visits.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Humanistic caring for patients in the operating room refers to providing the whole process of caring medical services for patients in the operating room. In order to standardize humanistic caring services for patients in the operating room of medical institutions, improve the comprehensive service level of the operating room, and enhance the surgical experience of patients, the Chinese Association for Life Care released the group standard " Humanistic Caring Management Standards for Patients in the Operating Room" in December 2023. This article interpreted the basic requirements for humanistic caring of patients in the operating room, the environment and facilities for humanistic caring, the procedures and measures for humanistic caring, and the quality management framework, aiming to assist administrators and clinical practitioners across various levels of medical institutions in accurately understanding and effectively implementing the standard, and to provide essential textual reference and practical guidance for promoting the application of the standard.

Objective:To determine the epidemiological characteristics of human bocavirus (HBoV) in children with acute respiratory infections (ARI) in Bengbu City, Anhui Province, in 2024.Methods:Nasopharyngeal swab samples were collected from 269 children with ARI in Bengbu City, Anhui Province, in 2024. Seventeen respiratory pathogens were screened using quantitative fluorescence PCR. For HBoV-positive samples, the VP1/VP2 structural gene fragments of HBoV were amplified and sequenced for genetic evolutionary analysis.Results:Among the 269 nasopharyngeal swab samples from children with ARI, the overall detection rate of respiratory pathogens was 48.33% (103/269). The top three pathogens with the highest detection rates were: Influenza A virus (FluA): 10.04% (27/269), Respiratory syncytial virus (RSV): 8.18% (22/269), Human bocavirus (HBoV): 7.43% (20/269). The age distribution of HBoV-infected children showed that the detection rate was highest in the 0-2 years age group (50%, 10/20), followed by the 3-5 years age group (25%, 5/20) and the over 6 years age group (25%, 5/20). However, there was no statistically significant difference in viral detection rates among the age groups. Genetic evolutionary analysis based on VP1/VP2 revealed that all 13 HBoV strains were of the HBoV-1 genotype.Conclusions:HBoV is one of the major pathogens causing ARI in children in Bengbu City, Anhui Province, in 2024, with HBoV-1 being the predominant genotype. Additionally, infants aged 0-2 years are the most susceptible population to HBoV infection.

Objective To quantitatively assess the association of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter (PM2.5) with residents mortality. Methods A time-stratified case-crossover study was conducted from 2020 to 2022 among 10606 non-accidental residents by using the Guangzhou Cause of Death Surveillance System in Conghua District, Guangzhou. Exposure levels of PAHs in PM_2.5 and meteorological data during the study period were obtained from the Center for Disease Control and Prevention in Conghua District and the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), respectively. Conditional Poisson regression model was used to estimate the exposure-response association between PAHs and the mortality risk. Results Fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene were significantly associated with an increased risk of mortality. For every one interquartile range increase in exposure levels, the non-accidental mortality risks increased by 8.33% (95% CI: 1.80%, 15.27%), 4.67% (95% CI: 1.86%, 7.57%), 6.07% (95% CI: 2.08%, 10.21%), 4.62% (95% CI: 1.85%, 7.47%), and 4.70% (95% CI: 0.53%, 9.03%), respectively. The estimated non accidental deaths attributable to exposure to fluoranthene, chrysene, benzo[k]fluorine, benzo[a]pyrene and indine[1,2,3-cd]pyrene were 5.91%, 6.08%, 6.51%, 6.46%, and 4.21%, respectively. Conclusions Short-term exposure to PAHs in PM_2.5, including fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene and indine[1,2,3-cd]pyrene, was significantly associated with an increased risk of mortality among residents.

Objective:To determine the epidemiological characteristics of human bocavirus (HBoV) in children with acute respiratory infections (ARI) in Bengbu City, Anhui Province, in 2024.Methods:Nasopharyngeal swab samples were collected from 269 children with ARI in Bengbu City, Anhui Province, in 2024. Seventeen respiratory pathogens were screened using quantitative fluorescence PCR. For HBoV-positive samples, the VP1/VP2 structural gene fragments of HBoV were amplified and sequenced for genetic evolutionary analysis.Results:Among the 269 nasopharyngeal swab samples from children with ARI, the overall detection rate of respiratory pathogens was 48.33% (103/269). The top three pathogens with the highest detection rates were: Influenza A virus (FluA): 10.04% (27/269), Respiratory syncytial virus (RSV): 8.18% (22/269), Human bocavirus (HBoV): 7.43% (20/269). The age distribution of HBoV-infected children showed that the detection rate was highest in the 0-2 years age group (50%, 10/20), followed by the 3-5 years age group (25%, 5/20) and the over 6 years age group (25%, 5/20). However, there was no statistically significant difference in viral detection rates among the age groups. Genetic evolutionary analysis based on VP1/VP2 revealed that all 13 HBoV strains were of the HBoV-1 genotype.Conclusions:HBoV is one of the major pathogens causing ARI in children in Bengbu City, Anhui Province, in 2024, with HBoV-1 being the predominant genotype. Additionally, infants aged 0-2 years are the most susceptible population to HBoV infection.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Humanistic caring for patients in the operating room refers to providing the whole process of caring medical services for patients in the operating room. In order to standardize humanistic caring services for patients in the operating room of medical institutions, improve the comprehensive service level of the operating room, and enhance the surgical experience of patients, the Chinese Association for Life Care released the group standard " Humanistic Caring Management Standards for Patients in the Operating Room" in December 2023. This article interpreted the basic requirements for humanistic caring of patients in the operating room, the environment and facilities for humanistic caring, the procedures and measures for humanistic caring, and the quality management framework, aiming to assist administrators and clinical practitioners across various levels of medical institutions in accurately understanding and effectively implementing the standard, and to provide essential textual reference and practical guidance for promoting the application of the standard.

The looming stimulus-evoked flight response to approaching predators is a defensive behavior in most animals. However, how looming stimuli are detected in the retina and transmitted to the brain remains unclear. Here, we report that a group of GABAergic retinal ganglion cells (RGCs) projecting to the superior colliculus (SC) transmit looming signals from the retina to the brain, mediating the looming-evoked flight behavior by releasing GABA. GAD2-Cre and vGAT-Cre transgenic mice were used in combination with Cre-activated anterograde or retrograde tracer viruses to map the inputs to specific GABAergic RGC circuits. Optogenetic technology was used to assess the function of SC-projecting GABAergic RGCs (scpgRGCs) in the SC. FDIO-DTA (Flp-dependent Double-Floxed Inverted Open reading frame-Diphtheria toxin) combined with the FLP (Florfenicol, Lincomycin & Prednisolone) approach was used to ablate or silence scpgRGCs. In the mouse retina, GABAergic RGCs project to different brain areas, including the SC. ScpgRGCs are monosynaptically connected to parvalbumin-positive SC neurons known to be required for the looming-evoked flight response. Optogenetic activation of scpgRGCs triggers GABA-mediated inhibition in SC neurons. Ablation or silencing of scpgRGCs compromises looming-evoked flight responses without affecting image-forming functions. Our study reveals that scpgRGCs control the looming-evoked flight response by regulating SC neurons via GABA, providing novel insight into the regulation of innate defensive behaviors.
Animals ; Superior Colliculi/physiology* ; Retinal Ganglion Cells/physiology* ; GABAergic Neurons/physiology* ; Mice, Transgenic ; Mice ; Optogenetics ; Visual Pathways/physiology* ; Mice, Inbred C57BL ; Photic Stimulation/methods* ; gamma-Aminobutyric Acid/metabolism* ; Male

Objective To investigate the effects of intrauterine perfusion with granulocyte colony-stimulating factor(G-CSF)on the endometrial thickness,volume,and blood flow parameters of patients with thin endometrium and their clinical outcomes.Methods We designed a prospective non-randomized synchronous controlled trial and recruited patients with thin endometrium who underwent frozen-thawed embryo transfer(FET)at Mianyang Central Hospital between September 1,2021 and September 1,2023.They were divided into two groups,an experimental group of patients who received the experimental treatment of intrauterine perfusion with G-CSF and a control group of patients who did not receive the experimental treatment.The general data and the clinical outcomes of the two groups were analyzed and compared.The endometrial thickness,volume and blood flow parameters of patients in the experimental group before and after intrauterine perfusion with G-CSF were analyzed.Results The clinical data of 83 patients were included in the study.The experimental group included 51 cases,while the control group included 31 cases.There were no significant differences in the baseline data between the two groups.The clinical pregnancy rate of the experimental group(56.86% )was higher than that of the control group(50.00% )and the rate of spontaneous abortion in the experimental group(27.59% )was lower than that in the control group(37.50% ),but the differences were not statistically significant(P>0.05).In the experimental group,the postperfusion endometrial thickness([0.67±0.1]cm)was greater than the preperfusion endometrial thickness([0.59±0.09]cm),the postperfusion([1.84±0.81]cm3)was greater than the preperfusion endometrial volume([1.54±0.69]cm3),and the postperfusion vascularization flow index(VFI)(1.97±2.82)was greater than the preperfusion VFI(0.99±1.04),with all the differences being statistically significant(P<0.05).Conclusion Intrauterine perfusion with G-CSF can enhance the endometrial thickness,volume,and some blood flow parameters in patients with thin endometrium.