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.

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

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.

Intraoperative neural monitoring (IONM) combines electrophysiology with anatomy to monitor nerve function during thyroid surgery,and has become an important auxiliary technology for neuroprotection. After more than ten years of development,the technology has been widely applied and popularized in China,promoting the development of a number of new technologies in the industry. Combined with the questionnaire survey data of the Chinese Neural Monitoring Study Group,this paper aims to summarize the clinical effect of IONM technology, the application status and existing problems at home and abroad, and propose the possible future development direction.

Objective:To evaluate the clinical rehabilitation effect of external counterpulsation(ECP)on patients with coronary heart disease(CHD)by meta-analysis.Methods:We searched the databases of CNKI,WanFang,VIP,CBM,PubMed,Web of Science,Cochrane Library and Embase for randomized controlled trials(RCTs)and prospective cohort studies upon rehabilitative effect of ECP on CHD patients before May 2024.And meta-analysis was conducted to calculate the pooled MD and 95％CI using the random(P＜0.5 or I2≥50％)or fixed effect models(other situations).Results:13 eligible literatures were finally included in the meta-analysis.Compared with participants in control group,those in trial group had significant higher left ventricular ejection fraction(MD=4.15,95％CI 2.55～5.76,P＜0.001),stroke volume(MD=9.11,95％CI 7.59～10.64,P＜0.001),peak oxygen uptake(MD=5.42,95％CI 2.53～8.32,P＜0.001),6-min walking distance(MD=31.14,95％CI 24.89～37.40,P＜0.001),metabolic equiv-alent(MD=0.58,95％CI 0.45～0.71,P＜0.001),exercise duration time(SMD=0.77,95％CI 0.55～0.99,P＜0.001),oxygen pulse(MD=0.88,95％CI 0.68～1.09,P＜0.001),and significant lower left ventricular end-diastolic diameter(MD=-3.19,95％CI-5.20～-2.61,P＜0.001).Conclusion:This study showed that ECP could effectively improve heart function,exercise capacity and tolerance of CHD patients.

Objective:To evaluate the clinical rehabilitation effect of external counterpulsation(ECP)on patients with coronary heart disease(CHD)by meta-analysis.Methods:We searched the databases of CNKI,WanFang,VIP,CBM,PubMed,Web of Science,Cochrane Library and Embase for randomized controlled trials(RCTs)and prospective cohort studies upon rehabilitative effect of ECP on CHD patients before May 2024.And meta-analysis was conducted to calculate the pooled MD and 95％CI using the random(P＜0.5 or I2≥50％)or fixed effect models(other situations).Results:13 eligible literatures were finally included in the meta-analysis.Compared with participants in control group,those in trial group had significant higher left ventricular ejection fraction(MD=4.15,95％CI 2.55～5.76,P＜0.001),stroke volume(MD=9.11,95％CI 7.59～10.64,P＜0.001),peak oxygen uptake(MD=5.42,95％CI 2.53～8.32,P＜0.001),6-min walking distance(MD=31.14,95％CI 24.89～37.40,P＜0.001),metabolic equiv-alent(MD=0.58,95％CI 0.45～0.71,P＜0.001),exercise duration time(SMD=0.77,95％CI 0.55～0.99,P＜0.001),oxygen pulse(MD=0.88,95％CI 0.68～1.09,P＜0.001),and significant lower left ventricular end-diastolic diameter(MD=-3.19,95％CI-5.20～-2.61,P＜0.001).Conclusion:This study showed that ECP could effectively improve heart function,exercise capacity and tolerance of CHD patients.

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.

Intraoperative neural monitoring (IONM) combines electrophysiology with anatomy to monitor nerve function during thyroid surgery,and has become an important auxiliary technology for neuroprotection. After more than ten years of development,the technology has been widely applied and popularized in China,promoting the development of a number of new technologies in the industry. Combined with the questionnaire survey data of the Chinese Neural Monitoring Study Group,this paper aims to summarize the clinical effect of IONM technology, the application status and existing problems at home and abroad, and propose the possible future development direction.

Objective:To investigate the effect of mitochondrial division of GABAergic neurons in substantia nigra pars reticulata(SNr)on motor dysfunction in mice with acute hepatic encephalopathy(AHE).Methods:AHE mice model was established by intraperitoneal injection of thioacetamide(TAA).The changes of liver lobules in AHE mice were observed by hematoxylin-eosin(HE)staining.The changes of serum aspartate aminotransferase(AST),alanine aminotransferase(ALT)and blood ammonia in AHE mice were detected by biochemical detection kit.Then,the motor function of AHE mice was observed by rod fatigue test,elevated cross maze test and open field test.Furthermore,the changes of mitochondrial area,perimeter,roundness and other morphological indicators in SNr of AHE mice were ob-served and analyzed by transmission electron microscopy.The expression of mitochondrial division and fusion related molecules in SNr of AHE mice was observed by Western Blot.Then,the expression of mitochondrial dynamic related protein 1(DRP1)in SNr of AHE mice was targeted by AAV virus.The mitochondrial membrane potential(MMP),ATP and reactive oxygen species(ROS)in SNr were detected by fluorescence enzyme marker,and the changes of motor function of mice were observed.Results:Compared with the control group,the motor function of AHE mice was signifi-cantly decreased,the mitochondrial division of SNr was significantly enhanced,and the expression of mitochondrial divi-sion related proteins was significantly increased.The MMP in SNr of AHE mice was significantly decreased,the ATP of cells was decreased,and the ROS was increased.After targeted inhibition of DRP1 expression in SNr of AHE mice,the movement was improved;further observation found that after the mitochondrial division in SNr of AHE mice was inhibi-ted,the MMP was significantly increased,the ATP of cells was increased,and the ROS was decreased,which demon-strated that the mitochondrial function was significantly improved.Conclusion:Targeted inhibition of mitochondrial di-vision of GABAergic neurons in SNr of AHE mice can improve mitochondrial morphology and function,thus alleviating their movement disorders.

italic>Lamiophlomis rotata is an important medicinal plant species endemic to the Tibetan Plateau, which is prone to strong climate change impacts on its habitable range due to the high sensitivity of the Tibetan Plateau to climate change. Accurate quantification of species vulnerability to climate change is essential for assessing species extinction risk and developing effective conservation strategies. Therefore, we carried out the α-shape analysis to determine the habitat of L. rotata. We then carried out the climate-niche factor analysis (CNFA) to assess the vulnerability of L. rotata to climate change based on five climate variables (i.e., mean diurnal range, temperature seasonality, mean temperature of warmest quarter, precipitation of driest month and precipitation of warmest quarter) in the context of two shared socioeconomic pathways (i.e., SSP126 and SSP585) and three global climate models (CMCC-ESM2: Centro Euro-Mediterraneo sui Cambiamenti Climatici-Earth System Model version 2; HadGEM3-GC31-LL: Hadley Global Environment Model version 3-Global Coupled configuration 3.1; IPSL-CM6A-LR: Institut Pierre Simon Laplace-Climate Model version 6) during two different periods (2041-2060 and 2081-2100). The vulnerability of L. rotata to climate change was calculated by integrating the sensitivity and exposure indices of L. rotata to five climate variables. The results showed that L. rotata had the highest vulnerability to the precipitation of warmest quarter. Its vulnerability within its habitat range generally showed a spatial pattern of high value in the southern region and low in the northern region, high in the western region and low in the eastern region. In general, the vulnerability of L. rotata under the SSP585 scenario was higher than that under the SSP126 scenario. The climate data of different global climate models have some influence on the results, while the resulted uncertainty can be reduced by data integration methods. As a result of climate change, the pressure on the survival of L. rotata in the future will be intensified in the low-altitude areas such as the Yarlung Zangbo River, Yigongzangbu River, Zayu River, and Jiaomuzu River, etc., while the highly weathered scree flats or stony alpine meadows in the high-altitude zones, such as the eastern Tanggula Mountain Range, the northern part of Hengduan Mountain Range, and the western part of the Qinling Mountains, may become its refuge. It is necessary to focus on and strengthen the protection and management of L. rotata resources in these vulnerble and critical areas.