Polymyxin is an essential antibiotic for treating multidrug-resistant Gram-negative bacterial infections； however， its significant nephrotoxicity greatly limits its clinical application. To enhance its safety and improve patient outcomes， the study of novel biomarkers for the early prediction of polymyxin-associated acute kidney injury is critically important. Novel biomarkers， such as cystatin C， kidney injury molecule-1， neutrophil gelatinase-associated lipocalin， N-acetyl-β-glucosaminidase， β2- microglobulin， have shown obvious advantages in the early prediction of polymyxin-associated acute kidney injury. Compared to traditional biomarkers， these biomarkers can provide sensitive and specific diagnostic information in the early stages of kidney injury， helping to optimize individualized treatment plans and reduce clinical risks. However， the high cost of detection and complex operation still limit their clinical promotion. Future research should focus on optimizing the detection technology of new biomarkers， simplifying the operation process and reducing costs， while conducting multi-center， large-scale randomized controlled trials to systematically evaluate the sensitivity and specificity of various novel biomarkers， in order to promote their application in the field of prediction of renal injury in clinical practice.

Polymyxin is an essential antibiotic for treating multidrug-resistant Gram-negative bacterial infections； however， its significant nephrotoxicity greatly limits its clinical application. To enhance its safety and improve patient outcomes， the study of novel biomarkers for the early prediction of polymyxin-associated acute kidney injury is critically important. Novel biomarkers， such as cystatin C， kidney injury molecule-1， neutrophil gelatinase-associated lipocalin， N-acetyl-β-glucosaminidase， β2- microglobulin， have shown obvious advantages in the early prediction of polymyxin-associated acute kidney injury. Compared to traditional biomarkers， these biomarkers can provide sensitive and specific diagnostic information in the early stages of kidney injury， helping to optimize individualized treatment plans and reduce clinical risks. However， the high cost of detection and complex operation still limit their clinical promotion. Future research should focus on optimizing the detection technology of new biomarkers， simplifying the operation process and reducing costs， while conducting multi-center， large-scale randomized controlled trials to systematically evaluate the sensitivity and specificity of various novel biomarkers， in order to promote their application in the field of prediction of renal injury in clinical practice.

ObjectiveTo investigate the current status of emergency management for severe mental disorders in Shanghai, and to provide countermeasures and suggestions for the establishment of a sound emergency management system for severe mental disorders and the enhancement of emergency management capability. MethodsA questionnaire survey and qualitative interviews were used to conduct an investigation into the emergency management in 17 district-level mental illness prevention and control institutions in Shanghai, which includes the basic situation of emergency management for severe mental disorders, the construction of emergency response teams and personnel, emergency preparedness drills and training, emergency management plans and rules and regulations, and problems encountered in emergency management. ResultsIn terms of emergency management mechanism and basic situation, resources such as personnel allocation, security funds and green channel were well equipped in each district-level mental illness prevention and control institution in Shanghai. However, the equipment of some hardware facilities was still insufficient to some extent. Therefore, further improvement on the emergency management mechanism for severe mental disorders was needed. With regard to the construction of emergency team and personnel allocation, the majority were those aged between 35‒<45 years old, with a bachelor’s degree, and more than 10 years of working experience. For example, 90.27% staff in district-level mental illness prevention and control institution had a bachelor’s degree or above, which was higher than that among the staff in community-level (73.60%); staff majored in clinical medicine in district-level institution accounted for the proportion at 52.71%, higher than that among the staff in community-level (28.86%); 57.24% staff in district-level institution had an intermediate professional title, higher than that among the staff in community-level (42.28%); and 69.90% staff in district-level institution had more than 10 years of working experience, higher than that among the staff in community-level (43.62%). In the aspect of emergency drills and training, all district-level mental illness prevention and control institutions in Shanghai had a high demand for emergency training, and the weak aspects mainly focused on lack of emergency service protocols, skills of addressing technical challenges, and construction of effectiveness evaluation system. Moreover, the teaching methods were primarily centered on case analysis, simulation drills, interactive discussions, and so forth. Concerning emergency management plans and rules and regulations, all districts in Shanghai had relatively established well-developed systems for emergency response plans, emergency response leadership groups, and emergency response operational task forces for severe mental disorders. About half of the institutions had established other rules and regulations related to emergency management of severe mental disorders in addition to emergency plans. ConclusionShanghai has initially established an emergency management system for severe mental disorders, but it is still fragile in specialized training for emergency management of severe mental disorders, construction of emergency management mechanisms, and the building-up of grassroots emergency teams. Further priorities should include strengthening emergency management training, enhancing the construction of emergency management personnel teams, and gradually establishing a more comprehensive and integrated emergency management mechanism for severe mental disorders.

This paper analyzed the traditional Chinese medicine （TCM） and western medical understanding of coronary microvascular disease （CMVD） from the three dimensions of "disease-syndrome-symptom". In western medicine， by summarizing the suspected diagnosis and understanding of CMVD， it is believed that inflammatory responses and vascular endothelial damage are the key mechanisms of the pathogenesis. From the perspective of TCM， the disease location is at blood， vessels and heart， and the fundamental cause is spleen and kidney depletion， closely realted to phlegm， stasis， toxin， wind and qi. Integrating the understanding of both TCM and western medicine， clinical treatment advocates taking the CMVD pathology as the base， and the TCM understanding of pathogenesis as the main focus. The properties of Chinese herbal medicinals is used as the guidance for medication， and the pharmacological understanding as the assisstance of treatment， with the medical history and the severity of the condition are additionally considered. It is finally proposed that during the acute phase， the methods of nourishing yin and resolving toxins， softening hardness and dissipating masses， dispelling wind and unblocking collaterals should be applied to alleviate the emergency. In the subacute phase， the focus should be on raising and lifting qi promote its movement， with flexible use of medicinals that can unblock yang. In the remission phase， the method of tonifying spleen and fortifying kidney should be used to maintain the stability of the condition.

Astrocytes are the largest glial population in the mammalian brain. However, we have a minimal understanding of astrocyte development, especially fate specification in different regions of the brain. Through lineage tracing of the progenitors of the third ventricle (3V) wall via in-utero electroporation in the embryonic mouse brain, we show the fate specification and migration pattern of astrocytes derived from radial glia along the 3V wall. Unexpectedly, radial glia located in different regions along the 3V wall of the diencephalon produce distinct cell types: radial glia in the upper region produce astrocytes and those in the lower region produce neurons in the diencephalon. With genetic fate mapping analysis, we reveal that the first population of astrocytes appears along the zona incerta in the diencephalon. Astrogenesis occurs at an early time point in the dorsal region relative to that in the ventral region of the developing diencephalon. With transcriptomic analysis of the region-specific 3V wall and lateral ventricle (LV) wall, we identified cohorts of differentially-expressed genes in the dorsal 3V wall compared to the ventral 3V wall and LV wall that may regulate astrogenesis in the dorsal diencephalon. Together, these results demonstrate that the generation of astrocytes shows a spatiotemporal pattern in the developing mouse diencephalon.
Mice ; Animals ; Astrocytes ; Neuroglia/physiology* ; Diencephalon ; Brain ; Neurons ; Mammals

Objective: To investigate the characteristics of schizophrenic patients using long-acting antipsychotic medications, so as to provide the basis for applicable population of long-acting antipsychotic medications. Methods: Data of schizophrenic patients using long-acting antipsychotic medications in Shanghai City from June 2020 to June 2022 were collected through Shanghai Mental Health Information Management System, and demographic characteristics, illness and medication use of patients were descriptively analyzed. Results: A total of 2 684 schizophrenic patients using long-acting antipsychotic medications were included in the study, had a mean age of (46.92±12.39) years, with 1 246 males (46.42%) and 1 438 females (53.58%). There were 1 397 unemployed cases, accounting for 52.05%; 1 429 cases with an educational level in junior high school or below, accounting for 53.24%; 1 301 unmarried cases, accounting for 48.47%; 832 cases in poverty, accounting for 31.00%. The caregivers of patients were mainly their parents, with 1 507 cases accounting for 56.15%. The courses of illness were mainly ≤10 years and >10-20 years, with 860 cases each, both accounting for 32.04%; 1 963 cases with incomplete self-awareness, accounting for 73.14%; 1 570 cases hospitalized at least once, accounting for 58.49%. There were 2 486 cases with continuous medication, accounting for 92.62%. The main method of taking medication was given by others, with 1 947 cases accounting for 72.54%. The medication adherence was mainly taking medication on time and in the right amount, with 2 437 cases accounting for 90.80%. Conclusion The main characteristics of schizophrenic patients using long-acting antipsychotic medications are young, unmarried, and unemployed adults, with incomplete self-awareness, continuous medication and medication given by others.

Purpose To establish a nomogram based on conventional ultrasound combined with contrast-enhanced ultrasound(CEUS)for predicting the probability of cervical central lymph node metastasis(CLNM)in clinical lymph node-negative(CN0)papillary thyroid carcinoma(PTC)patients.Materials and Methods A retrospective study was performed on 359 patients with single CN0 PTC,all of whom underwent thyroid surgery and prophylactic central compartment neck dissection in the First Affiliated Hospital of Shihezi University from September 2015 to March 2022.According to the postoperative pathological results,there were 116 cases with CLNM(+)and other 243 cases with CLNM(-).The indicators of gender,age,conventional ultrasound and CEUS were recorded,and multivariate stepwise Logistic regression was performed to screen out risk predictors to construct prediction models for CLNM in CN0 PTC.The receiver operating characteristic curves of prediction models were drawn,and the area under the curve(AUC)was further compared.The preferable prediction model was selected to establish the risk probability nomogram,and the prediction performance and clinical applicability of the nomogram model were assessed.Results Multivariate analysis showed that gender,age,the maximum diameter of nodule,capsule invasion and enhancement pattern on CEUS were risk factors for CLNM in CN0 PTC(all P<0.05).The AUC of prediction model 1 including the above five indicators was 0.753,and the AUC of prediction model 2 excluding CEUS indicator was 0.704.There were statistically significant difference in AUCs between the two models(Z=2.473,P=0.013).Prediction model 1 was selected to construct a risk probability nomogram for predicting CLNM in CN0 PTC.The nomogram had a C-index of 0.753 and showed well consistency on the calibration curve.Clinical decision curve analysis indicated that the nomogram could achieve ideal net benefit when the threshold probability was between 10.7%to 81.5%.Conclusion Gender,age,the maximum diameter of nodule,capsule invasion and enhancement pattern on CEUS may be the risk predictors for CLNM in CN0 PTC.The nomogram model based on the above indicators can predict the probability of CLNM effectively,and the CEUS indicators can substantially improve the prediction performance of the model.

Objective In this study,we analyzed the transcriptome sequences of Kupffer cells exposed to simulated microgravity for 3 d and conducted biological experiments to determine how microgravity initiates apoptosis in Kupffer cells. Methods Rotary cell culture system was used to construct a simulated microgravity model.GO and KEGG analyses were conducted using the DAVID database.GSEA was performed using the R language.The STRING database was used to conduct PPI analysis.qPCR was used to measure the IL1B,TNFA,CASP3,CASP9,and BCL2L11 mRNA expressions.Western Blotting was performed to detect the level of proteins CASP3 and CASP 9.Flow cytometry was used to detect apoptosis and mitochondrial membrane cells.Transmission electron microscopy was used to detect changes in the ultrastructure of Kupffer cells. Results Transcriptome Sequencing indicated that simulated microgravity affected apoptosis and the inflammatory state of Kupffer cells.Simulated microgravity improved the CASP3,CASP9,and BCL2L11 expressions in Kupffer cells.Annexin-V/PI and JC-1 assays showed that simulated microgravity promoted apoptosis in Kupffer cells.Simulated microgravity causes M1 polarization in Kupffer cells. Conclusion Our study found that simulated microgravity facilitated the apoptosis of Kupffer cells through the mitochondrial pathway and activated Kupffer cells into M1 polarization,which can secrete TNFA to promote apoptosis.

To address the key challenges in multivariate statistical modeling,a higher-order derivative approach combined with vector space angle multiplicative error correction was proposed for establishing an acid value prediction ordinary least squares(OLS)regression model based on attenuated total reflectance-Fourier transform infrared(ATR-FTIR)spectroscopy.By using acid values measured by potentiometric titration as reference,ATR-FTIR spectroscopy was utilized for direct calibration and prediction of acid values on 96 kinds of lubricating oil samples from a wind turbine.Firstly,the simulated hyperbolic(SH)method was employed to obtain accurate fourth derivative spectrum,resolving overlapping bands and enhancing spectral selectivity.Then,from the calibration set(48 samples),informative spectral regions were identified based on correlation coefficients.Next,the sample with the highest acid value was selected as the reference and1/(1+tan(θ/2))was used as the metric relation of the spectrum to suppress the multiplicity error caused by factors such as the change of effective optical path in ATR-FTIR spectroscopy.After pretreatment of the spectrum by the method of fourth-order derivative combined with angular quantity,the number of variables decreased from 1737 to 8,and the matrix condition number decreased from 1.85×1015 to 56.34,which effectively eliminated the collinearity issue for OLS regression.Direct OLS modeling on spectral preprocessed data achieved a determination coefficient of 0.981 for 47 validation samples,with a relative error range of-8.38%-8.22%,outperforming the commonly used partial least squares(PLS)method(Determination coefficient of 0.865,relative error of-27.82%-22.38%).It was proved that effective data preprocessing significantly improved the prediction accuracy of the model.Furthermore,when the number of calibration set was compressed to 25 and the number of validation set was expanded to 70,the model retained 8 variables with a condition number of 42.60,the determination coefficient of validation set was 0.972,and the relative error ranged from-10.80%to 12.31%.Comparing with the PLS method(Determination coefficient of 0.724,relative error of-34.26%-53.84%),the improvement was more obvious,which showed that the method could still have high prediction accuracy even with fewer modeling samples as well as robustness against multiplicative error interference.

Objective:To investigate the alterations in brain dynamic functional network connectivity (dFNC) and their significance in Parkinson's disease (PD) patients with postural instability/gait difficulty (PIGD).Methods:Ninety PD patients admitted to Department of Neurology, First Affiliated Hospital of Nanjing Medical University from May 2016 to August 2019 were recruited, and 54 healthy controls matched with gender and age were chosen; their clinical data and resting-state functional MRI (rs-fMRI) data were collected. PD patients were divided into PD with PIGD (PD-PIGD) group ( n=49) and PD without PIGD (PD-non-PIGD) group ( n=41) according to Unified Parkinson's Disease Rating Scale (UPDRS) scores. Independent component analysis (ICA), sliding window method and k-means clustering were used to analyze the dFNC and compare among groups. Correlations of dFNC alterations with clinical scales were verified by partial correlation analysis. Results:Four repeated recurring functional connectivity states were identified, and PD-PIGD patients had high frequency in state 3 (44%) and state 2 (23%) of the low dFNC. In terms of dFNC time attributes, PD-PIGD patients had longer mean dwell time in state 3 than PD-non-PIGD patients and had lower number of transitions in state 3 than PD-non-PIGD patients and healthy controls, with significant differences ( P<0.05); PD-PIGD patients had significantly higher fractional windows and statistically longer mean dwell time in state 2 than healthy controls ( P<0.05). In terms of dFNC strengths, compared with healthy controls, PD-PIGD patients showed significantly decreased functional connectivity within default mode network (DMN, between medial superior frontal gyrus and precuneus) and auditory network (AN, between superior temporal gyrus and middle temporal gyrus), but significantly increased functional connectivity between sensorimotor network (SMN, supplementary motor area) and DMN (precuneus) in state 2 ( P<0.05, false discovery rate [FDR]-corrected). Partial correlation analysis indicated positive correlation between mean dwell time in state 3 and PIGD scores in PD-PIGD patients ( r=0.450, P=0.039). Conclusion:PD-PIGD patients exhibit specific dFNC, mainly characterized by low connectivity of the brain functional network and prolonged dwell time; local functional network domains often separate between DMN, AN and SMN networks and within the networks.