Objective To investigate the value of preoperative clinical data and computed tomography angiography (CTA) data in predicting perioperative mortality risk in patients with acute aortic dissection (AAD), and to construct a Nomogram prediction model. Methods A retrospective study was conducted on AAD patients treated at Affiliated Hospital of Zunyi Medical University from February 2013 to July 2023. Patients who died during the perioperative period were included in the death group, and those who improved during the same period were randomly selected as the non-death group using a random number table method. The first CTA data and preoperative clinical data within the perioperative period of the two groups were collected, and related risk factors were analyzed to screen out independent predictive factors for perioperative death. The Nomogram prediction model for perioperative mortality risk in AAD patients was constructed using the screened independent predictive factors, and the effect of the Nomogram was evaluated by calibration curves and area under the curve (AUC). Results A total of 270 AAD patients were included. There were 60 patients in the death group, including 42 males and 18 females with an average age of (56.89±13.42) years. There were 210 patients in the non-death group, including 163 males and 47 females with an average age of (56.15±13.77) years. Multivariate logistic regression analysis showed that type A AAD [OR=4.589, 95%CI (2.273, 9.267), P<0.001], irregular tear morphology [OR=2.054, 95%CI (1.025, 4.117), P=0.042], decreased hemoglobin [OR=0.983, 95%CI (0.971, 0.995), P=0.007], increased uric acid [OR=1.003, 95%CI (1.001, 1.005), P=0.004], and increased aspartate aminotransferase [OR=1.003, 95%CI (1.000, 1.006), P=0.035] were independent risk factors for perioperative death in AAD patients. The Nomogram prediction model constructed using the above risk factors had an AUC of 0.790 for predicting perioperative death, indicating good predictive performance. Conclusion Type A AAD, irregular tear morphology, decreased hemoglobin, increased uric acid, and increased aspartate aminotransferase are independent predictive factors for perioperative death in AAD patients. The Nomogram prediction model constructed using these factors can help assess the perioperative mortality risk of AAD patients.

Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC)based on preoperative lipid ratios, cM0(i+)staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+)stage or cM0 stage according to postoperative circulating tumor cells(CTCs)test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC)curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR)and cM0(i+)staging:Group A [LNLR＞1.68 and diagnosed with cM0(i+)], Group B [LNLR＞1.68 or diagnosed with cM0(i+)], and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS)among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS, and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1. 68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR, cM0(i+)staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC)for 1-, 3-, and 5-year RFS was 0.896(95% CI:0.8121-0.9627), 0.890(95%CI:0.7879-0.9641), and 0.870(95%CI:0.7697-0.9526), indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR, cM0(i+)staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

In recent years, the study of single-cell transcriptome sequencing technology in the heterogeneity of astrocytes (astrocytes) after spinal cord injury (SCI) has provided new perspectives on post-traumatic nerve regeneration and repair. To provide a review on the research progress of single-cell sequencing technology in astrocytes after spinal cord injury (SCI), and to more comprehensively and deeply elaborate the application of single-cell sequencing technology in the field of astrocytes after SCI. Single-cell sequencing technology can analyse the transcriptomes of individual cells in a high-throughput manner, thus revealing fine differences in cell types and states. By using single-cell sequencing technology, the heterogeneity of astrocytes after SCI and their association with nerve regeneration and repair were revealed. In conclusion, the application of single-cell sequencing technology provides an important tool to reveal the heterogeneity of astrocytes after SCI, to further explore the mechanisms of astrocytes in SCI, and to develop intervention strategies targeting their regulatory mechanisms in order to improve the therapeutic efficacy of SCI. The discovery of changes in astrocyte transcriptome dynamics has improved researchers' understanding of spinal cord injury lesion progression and provided new insights into the treatment of spinal cord injury at different time points. To date, all of these findings need to be validated by more basic research and sufficient clinical trials. In the future, single-cell sequencing technology, through interdisciplinary collaboration with bioinformatics, computer science, tissue engineering, and clinical medicine, is expected to open a new window for the treatment of spinal cord injury.
Spinal Cord Injuries/metabolism* ; Astrocytes/cytology* ; Single-Cell Analysis/methods* ; Humans ; Animals ; Transcriptome ; Nerve Regeneration

After the wooden shipwreck was recovered from the marine underwater environment,the wooden components undergo varying degrees of degradation,therefore,accurately determining the extent of degradation is a fundamental scientific issue for implementing effective preservation strategies.In this work,the wooden remains of Pinus massoniana excavated from the"Nanhai No.1"shipwreck(Southern Song Dynasty)were investigated and compared with the modern wood to discriminate the degradation levels of archaeological wood using attenuated total reflection Fourier transform infrared(ATR-FTIR)spectroscopy.The residual sugar content within wood cell walls was determined using a non-invasive automated microscale ATR-FTIR method to extract chemical information from the wood tangential section.Microstructural characterization of wood samples was conducted by super depth of field microscopy and scanning electron microscopy.FTIR spectral analysis was performed to evaluate the degradation state and elucidate changes in cellulose crystallinity.Finally,the combination of FTIR spectroscopy with the sparse partial least squares discriminant analysis(sPLS-DA)model facilitated the rapid discrimination of degradation levels in shipwreck archaeological wood,and the performance of the model was evaluated using receiver operating characteristic(ROC)curves and area under the curve(AUC).The results showed that the higher the degree of wood degradation,the lower the residual sugar content in the wood cell wall,and the residual glucose content of highly degraded wood was only 4.7％.Significant differences were observed in both the tangential section microstructure and FTIR characteristic absorption patterns across degradation levels,and as the degradation advanced,progressive cell wall loosening occurred alongside selective removal of polysaccharide components,and the relative lignin content was increased,resulting in an elevated A1509/A1370 ratio in FTIR spectra.The sPLS-DA model achieved excellent discrimination performance with AUC values exceeding 0.9,confirming that the combination of FTIR spectroscopy with sPLS-DA enabled accurate assessment of degradation levels in shipwreck archaeological wood.This study developed a rapid and accurate methodology for assessing degradation levels in shipwreck archaeological wood based on microscale ATR-FTIR spectroscopy,which would help to promote the accurate assessment of the preservation state of waterlogged wooden artifacts.

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To explore the clinical application of mobile-intelligent medical management system for gout patients.Methods:This study was a randomized controlled trial. One hundred and two patients with gout who visited rheumatology department of the First Hospital of Jilin University from January 2022 to October 2023 were randomly divided into study group ( n=53) and the control group ( n=49). Patients in the study group were managed by the mobil-gout(M-G) health management system which consists of a distant management plateform and an App. The body weight and blood uric acid levels were regularly self-tested by patients, and the information was sent to the system, the doctor gave feed-back advice on medication adjustment and dietary management every 2 weeks. While patients in the control group were managed following the conventional outpatient consultation mode. The serum uric acid levels, liver and kidney function tests and physical examinations were conducted on the day of enrollment and 3 and 6 months after treatment. The number of affected joints and the frequency of attacks were evaluated, and pain was assessed using the visual analog scale (VAS). The efficacy of treatment were compared between two groups. Results:After 3 and 6 months of treatment, the serum uric acid levels in study group were significantly lower than those in control group (408.0 (345.0, 450.0)μmol/L vs. 458.0 (409.0, 482.0)μmol/L, 371.0 (338.5, 409.0) μmol/L vs. 412.0 (368.5, 445.0)μmol/L; Z=3.37, 4.61, P<0.01); and VAS scores were also significantly lower than those in control group (6.66±1.46 vs. 4.68±1.80, 7.45±1.06 vs. 5.88±1.52; t=14.12, 13.72, P<0.01). After 6 months of treatment, the frequency of gout attacks in the study group was significantly lower than that in the control group ( χ2=9.30, P<0.05). As the follow-up period was extended, the average number of joints affected by gout attacks and the frequency of gout attacks in the study group showed a reducing trend compared to the control group, but the differences were not statistically significant ( P>0.05). Conclusions:The M-G medical management system can more effectively control uric acid levels and alleviate symptome for gout patients.

Objective: To develop a modified calculation formula for renal tumor volume and tumor contact surface area (CSA) based on the modeling results of 3D Slicer software, and to create a webpage of the calculation formula for use. Methods: The general information and tumor anatomical data of 98 patients who underwent partial nephrectomy during Jan.2021 and Jul.2023 in the Second Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed.The imaging data were input into 3D Slicer software in the form of Dicom files for tumor and ipsilateral kidney modeling to obtain tumor anatomical data.The relationship between tumor anatomical parameters and tumor volume and CSA was analyzed using multifactorial linear regression.The initial modified formulas (V2, C2) and the optimized modified formulas (V3, C3) for tumor volume over CSA were established, respectively, after insignificant variables were eliminated.The mean square error (MSE) and Akaike information criterion (AIC) of the modified and traditional formulas (V1, C1) were compared, and the formula with the smallest MSE and AIC was selected as the optimal tumor volume and CSA calculation formula.The median tumor volume and CSA obtained from 3D modeling were used as the cutoff values.The optimal formula and conventional formula were applied to calculate tumor volume and CSA for all patients, and risk stratification was performed for all patients based on these cutoff values, and the perioperative indicators of patients in the upper and lower groups were compared.Finally, an online calculation tool was developed based on HTML. Results: Based on multifactorial linear regression analysis, we obtained the modified tumor volume calculation formula: V=0.382abc+2.488a+2.372b－4.146c+1.948（V2）, V=0.469abc－4.586c+13.816（V3）; the modified tumor CSA calculation formula CSA=2.469a －2.262L －19.23a+6.206b+1.212c+18.017L+1.616h－3.97h －2.185h/h －0.388（C2）, CSA=2.376a －2.144L －20.157a+5.024b+1.128c+17.578L+2.525h－2.634（C3）.Both of the modified volume formula (MSE=151.298 vs. 127.807 vs. 104.106) and modified CSA formula (MSE=309.878 vs.23.556 vs.30.388) had smaller errors compared to the conventional formula.The modified volume calculation formula showed that bleeding was more and thermal ischemia time was longer in patients with larger tumor volumes than in patients with smaller tumor volumes (P<0.05); and the modified CSA calculation formula showed that bleeding was more, surgery and thermal ischemia time were longer in patients with high CSA than in patients with low CSA (P＜0.05).Finally, V3 and C3 are selected as the best calculation formula, and a web page (https://lizihao-bot.github.io/RCC-Calculate/) was established for easy use. Conclusion: This study combined data from a medical information technology platform with numerical modeling methods to provide a faster and more accurate method to calculate the renal tumor volume and CSA.Meanwhile, a webpage version of the tool was developed to enhance its practicability.

Treatment based on meridian differentiation is a characteristic method in clinical acupuncture diagnosis and treatment. Accurately defining and explaining its main content and core concepts is essential for effective clinical guidance. This paper reviews the historical and contemporary understanding, concepts, and primary content of treatment based on meridian differentiation. It proposes a four-step process for clinical application: meridian examination, treatment based on meridian differentiation, acupoint selection, and appropriate treatment methods, with TCM syndrome differentiation applied throughout. Constructing a diagnostic and treatment system which is based on meridian differentiation and suited to clinical acupuncture is significant for enhancing therapeutic efficacy and maximizing the benefits of acupuncture in disease treatment.
Humans ; Meridians ; Acupuncture Therapy/methods* ; Acupuncture Points ; Medicine, Chinese Traditional