Sexual dimorphism in the brain underlies behavioral differences between sexes. The bed nucleus of the stria terminalis (BNST) is a complex nucleus that differs between males and females, but the sexual dimorphism in cytoarchitecture and the connectome of its oval subdivision (ovBNST) remains largely unexplored. By combining snRNA-seq and transgenic labeling, we found a higher density of ovBNST proenkephalin (ovBNST^PENK) neurons in male than female mice. Anatomically, we virally mapped the efferents and afferents of ovBNST^PENK neurons, finding reciprocally dimorphic connections with the hypothalamus and striatum. Gene enrichment analysis suggests that ovBNST^PENK neurons are modulated by the upstream dopamine pathway. Functionally, by applying caspase-3-mediated depletion of ovBNST^PENK neurons, we found that loss of these neurons enhanced locomotor activity in male but not female mice, without altering the anxiety-like phenotypes in either sex. Our study may pave the way for a better understanding of the anatomical and functional profiles of ovBNST^PENK neurons from a sexually dimorphic perspective.
Animals ; Male ; Female ; Septal Nuclei/physiology* ; Sex Characteristics ; Neurons/physiology* ; Enkephalins/metabolism* ; Mice ; Mice, Transgenic ; Protein Precursors/metabolism* ; Mice, Inbred C57BL ; Neural Pathways/physiology*

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 analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of 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.

Objective:To evaluate the utility of ultrasonographic monitoring of the rectus femoris muscle—specifically, the rates of change in thickness and cross-sectional area (CSA)—in assessing nutritional status and long-term functional outcomes in patients with sepsis.Methods:In this prospective observational study, sepsis patients admitted to the ICU of the Second Affiliated Hospital of Soochow University between October 2023 and October 2024 were classified by nutritional status at discharge using the Global Leadership Initiative on Malnutrition (GLIM) criteria. Differences in serial ultrasound-measured rectus femoris thickness and CSA on days 1, 3, 5, and 7 were compared between malnourished and non-malnourished groups. The predictive value of these ultrasound parameters for malnutrition was analyzed. Functional prognosis was assessed using the Sarcopenia Assessment Scale, Short Physical Performance Battery, and Manual Muscle Testing, with correlations to muscle changes examined.Results:Of the 71 enrolled patients (median age 73.00 [ IQR: 61.00–80.00]; 47.89% female, 52.11% male), those with malnutrition showed significantly greater variation rates in rectus femoris thickness and CSA on days 3, 5, and 7 compared to the non-malnourished group ( P < 0.05). ROC analysis revealed that the day-7 CSA variation rate had the highest predictive value for malnutrition (AUC = 0.817, 95% CI: 0.713-0.930). These muscle variation rates also correlated strongly with conventional nutritional markers such as BMI, albumin, and urea. Similarly, patients with impaired functional outcomes exhibited higher variation rates in muscle parameters on days 3, 5, and 7 ( P < 0.05), with the day-7 CSA variation rate being most predictive of functional prognosis (AUC = 0.749, 95% CI: 0.632-0.867). Conclusions:Ultrasonographic assessment of rectus femoris thickness and CSA variation rates provides a valuable tool for evaluating nutritional status and predicting functional prognosis in sepsis patients, outperforming traditional biomarkers. This method shows promise for guiding individualized nutrition support and rehabilitation strategies to improve long-term outcomes.

ObjectiveTaking the oligosaccharides in Rehmanniae Radix（RR） as the research object， the content determination method based on high performance liquid chromatography-evaporative light scattering detection（HPLC-ELSD） and thin layer chromatography（TLC） identification method were established to explore the content and distribution of oligosaccharides in different RR herbs and decoction pieces. MethodA total of 10 batches of fresh and raw RR， 12 batches of RR decoction pieces and Rehmanniae Radix Praeparata（RRP） were collected. A TLC identification method for fructose， sucrose， manninotriose， raffinose and stachyose in RR was established by using silica gel G thin-layer plates with ethyl acetate-water-anhydrous formic acid-glacial acetic acid（12∶6∶5∶4） as the developing agent and 10% sulfuric acid-ethanol solution as chromogenic agent. A HPLC-ELSD was used to determine the contents of fructose， glucose， sucrose， melibiose， raffinose， manninotriose and stachyose in different RR herbs and decoction pieces. Then principal component analysis（PCA） and partial least squares-discriminant analysis（PLS-DA） were used to analyze the contents of 7 kinds of saccharides in RR herbs and decoction pieces， and the differential components were screened with the value of variable importance in the projection（VIP）>1. ResultThe results of TLC identification showed that fresh RR， raw RR and its decoction pieces showed spots of the same color on the corresponding positions with the control products of stachyose， raffinose and sucrose， while the TLC of RRP showed spots of the same color at corresponding positions to manninotriose and fructose controls. The results of methodological investigations of 7 analytes met the requirements of determination. Only glucose， sucrose， raffinose and stachyose were detected in 10 batches of fresh RR and 10 batches of raw RR herbs， the average contents of which were 0.84%， 4.62%， 2.42% and 57.90% in fresh samples， while those were 3.16%， 9.36%， 7.05% and 38.10% in raw samples， respectively. In 12 batches of RR decoction pieces， the contents of the above seven sugars（fructose， glucose， sucrose， melibiose， raffinose， manninotriose and stachyose） were 1.68%， 4.27%， 9.96%， 0.53%， 6.85%， 3.05% and 37.52%， respectively. In 12 batches of RRP， the contents of the above seven sugars were 10.62%， 11.01%， 1.25%， 3.35%， 1.12%， 28.16% and 6.39%， respectively. The results of multivariate statistical analysis showed that fresh RR， raw RR and RRP could be distinguished from each other by the contents of the 7 sugars， and the main differential components were stachyose， sucrose， raffinose and manninotriose. ConclusionIn terms of oligosaccharides， the contents and types of saccharides in different herbs and decoction pieces of RR are quite different， and the TLC identification method based on this can be used to distinguish raw RR from RRP， which can lay a foundation for improving the quality standard of RR and developing and applying oligosaccharides in different processed products of RR.

Objective To systematically evaluate the control effects of different concentrations of atropine on myopia in Chinese children.Methods PubMed,Embase,The Cochrane Library,Web of Science,CBM,WanFang Data,VIP and CNKI databases were retrieved to collect the studies on children's myopia control by atropine from the establishment of the database to April 2023.After the literature screening,data extraction and bias risk valuation were carried out by 2 research-ers,a Meta-analysis was performed via RevMan 5.4 software.Results A total of 32 studies were included,comparing the effects of 7 different concentrations of atropine and placebo.The Meta-analysis showed that compared with placebo,0.1 g·L-1 atropine had a significant impact on the change of spherical equivalent[MD=0.39,95％CI(0.26,0.52),P＜0.05],and significantly suppressed the axial length increment[MD=-0.18,95％CI(-0.24,-0.12),P＜0.05].Among other concentrations,0.2g·L-1,0.5g·L-1 and 10 g·L-1 atropine had sound effects on myopia control.Conclusion Exist-ing evidence shows that compared to placebo,atropine at concentrations of0.1 g·L-1,0.2 g·L-1,0.5 g·L-1and 10 g·L-1 has better effects on controlling the spherical equivalent and axial length of children with myopia.Among them,at-ropine at the concentration of 0.1 g·L1 may have the best effect.

Objective To investigate the changes of sex hormone levels in polycystic ovary syndrome(PCOS)in infertile population after the assisted reproductive technology treatment,and to provide an evidence for the choice of the treatment.Methods The medical data of patients admitted to the First Affiliated Hospital of Kunming Medical University from January 2016 to June 2021 were collected and divided into PCOS group(103)and non-PCOS group(589)according to whether they were diagnosed with PCOS,and the sex hormone changes of the two groups were compared.Results The patients in PCOS group were younger and had the higher BMI,more sinus follicles,higher AMH value,and lower total Gn usage.The number of LH/FSH>2 in PCOS group was higher than that in non-PCOS group(P<0.05).After the treatment,LH in both groups decreased,FSH,E2 and(P<0.05)increased;The difference of LH and E2 before and after the treatment in PCOS group was greater than that in non-PCOS group<0.05).Conclusion Compared with non-PCOS infertile patients,the changes of sex hormone indexes in PCOS infertile patients before and after the treatment were more obvious.In order to obtain the better clinical effect in patients with polycystic ovaries,it is recommended to pay attention to the changes of related sex hormone levels in the course of subsequent treatment,and choose a reasonable treatment plan.

Objective To construct the medical risk comprehensive assessment system based on big data,and to evaluate its consistency and efficiency.Methods Aiming at the current situation of risk assessment of inpatients,based on the means of big data,the medical natural language processing was used to design a medi-cal risk comprehensive assessment system.The system can automatically capture various data of patients,au-tomatically generate the scores by data mining and machine learning technology and send the risk data to med-ical staff,so as to realize the automation and intellectualization.The randomized controlled analysis was used to conduct the manual scoring and machine scoring for included the score scale.The visual risk matrix diagram was automatically generated by comparing the scoring.Results The Kappa values of the scoring system in the included study of the system were as follows:the Kappa value in Caprini scale(surgery)and Padua scale(internal medicine)was 1.00,NNIS Kappa value was 1.00,Nomogram Kappa value was 0.87,Kappa value in the Morse assessment scale/Hendrich model was 0.83,Braden Kappa value was 0.80,ASA 2023 Kappa was 1.00 and NRS 2002 Kappa value was 0.90.The taking time in the machine scoring all were shorter than those in the manual scoring,and the difference was statistically significant(P＜0.05).Conclusion The risk matrix graph constructed by this system could sharply increase the evaluation efficiency and accuracy,which not only provide the accuracy diagnosis and treatment regimen,but also shorten the hospitalization duration and reduce the medical costs.