ObjectiveTo analyze the differences in color， odor and volatile components of Citri Reticulatae Pericarpium（CRP） before and after being stir-fried with Halloysitum Rubrum， and to explore the material basis of enhancing the effect of strengthening spleen after processing and the scientific connotation of decoction pieces processed with Halloysitum Rubrum as the auxiliary material. MethodsThe volatile components of the samples before and after processing were identified and relatively quantified by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the volatile components were analyzed by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）. According to the principle of variable importance in the projection（VIP） value>1.5， volatile differential components before and after processing were screened. And combined with intelligent sensory technologies such as colorimeter and electronic nose， the chroma and odor information of CRP before and after being stir-fried with Halloysitum Rubrum were identified. Pearson correlation analysis was used to explore the correlation between volatile differential components and chroma values. ResultsA total of 112 volatile components were identified from CRP and CRP stir-fried with Halloysitum Rubrum， of which 84 were from CRP and 97 were from CRP stir-fried with Halloysitum Rubrum. And 7 differential components were selected， including α-pinene， β-myrcene， linalool， sabinene， ocimene isomer mixture， A-ocimene， and δ-elemene. After being processed with Halloysitum Rubrum， the brightness value（L^*）， yellow-blue value（b^*） and total chromatic value（E^*_ab） of CRP were decreased（P<0.01）， and red-green value（a^*） was increased（P<0.01）， the response values of S4， S5， S10 and S13 sensors were significantly increased（P<0.05）， and the response values of S3 and S8 sensors were significantly decreased（P<0.05）. Correlation analysis showed that α-pinene and β-myrcene were negatively correlated with L^* and E^*_ab， but positively correlated with a^*. Sabinene was positively correlated with L^* and E^*_ab. Linalool was positively correlated with L^* and E^*_ab， and negatively correlated with a^*. The ocimene isomer mixture was positively correlated with the L^*. ConclusionAfter being processed with Halloysitum Rubrum， the appearance color， odor and volatile components of CRP change significantly， and α-pinene， β-myrcene， sabinene， linalool and A-ocimene are the characteristic volatile components before and after processing， which can provide references for the quality evaluation and clinical application of CRP and its processed products.

ObjectiveTo analyze the differences in color， odor and volatile components of Citri Reticulatae Pericarpium（CRP） before and after being stir-fried with Halloysitum Rubrum， and to explore the material basis of enhancing the effect of strengthening spleen after processing and the scientific connotation of decoction pieces processed with Halloysitum Rubrum as the auxiliary material. MethodsThe volatile components of the samples before and after processing were identified and relatively quantified by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the volatile components were analyzed by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）. According to the principle of variable importance in the projection（VIP） value>1.5， volatile differential components before and after processing were screened. And combined with intelligent sensory technologies such as colorimeter and electronic nose， the chroma and odor information of CRP before and after being stir-fried with Halloysitum Rubrum were identified. Pearson correlation analysis was used to explore the correlation between volatile differential components and chroma values. ResultsA total of 112 volatile components were identified from CRP and CRP stir-fried with Halloysitum Rubrum， of which 84 were from CRP and 97 were from CRP stir-fried with Halloysitum Rubrum. And 7 differential components were selected， including α-pinene， β-myrcene， linalool， sabinene， ocimene isomer mixture， A-ocimene， and δ-elemene. After being processed with Halloysitum Rubrum， the brightness value（L^*）， yellow-blue value（b^*） and total chromatic value（E^*_ab） of CRP were decreased（P<0.01）， and red-green value（a^*） was increased（P<0.01）， the response values of S4， S5， S10 and S13 sensors were significantly increased（P<0.05）， and the response values of S3 and S8 sensors were significantly decreased（P<0.05）. Correlation analysis showed that α-pinene and β-myrcene were negatively correlated with L^* and E^*_ab， but positively correlated with a^*. Sabinene was positively correlated with L^* and E^*_ab. Linalool was positively correlated with L^* and E^*_ab， and negatively correlated with a^*. The ocimene isomer mixture was positively correlated with the L^*. ConclusionAfter being processed with Halloysitum Rubrum， the appearance color， odor and volatile components of CRP change significantly， and α-pinene， β-myrcene， sabinene， linalool and A-ocimene are the characteristic volatile components before and after processing， which can provide references for the quality evaluation and clinical application of CRP and its processed products.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

Objective: To comprehensively evaluate the current alanine aminotransferase (ALT) screening strategies and provide a basis for their optimization. Methods: ALT test results of 21 345 blood samples were collected from 33 blood collection institutions. Multiple probability distribution functions were employed to fit the data, and the akaike information criterion (AIC) was used to determine the optimal fitting model. Based on this model, 1 million random samplings were conducted to simulate the final ALT test results of blood donors under different ALT screening strategies, eligibility criteria, and pre-donation ALT detection deviations. A decision tree was subsequently constructed for health economic analysis. Results: The log-normal distribution with a mean of 2.96 and a variance of 0.65 provided the best fit for the data. When the eligibility criteria was 50 U/L and the pre-donation detection deviation was ±20%, not conducting pre-donation testing increased blood donation by 1.14%. When the pre-donation detection deviation was ±20% and the eligibility criteria was raised from 50 U/L to 100 U/L, conducting and not conducting pre-donation testing increased blood donation by 7.59% and 6.60%, respectively. With a eligibility criteria of 50 U/L and a pre-donation detection deviation of ±20%, 1.14% of eligible blood donors would be disqualified from donating blood. Health economic analysis showed that when the eligibility criteria was adjusted to 56 U/L or higher, not conducting pre-donation ALT testing was the dominant strategy; under other conditions, conducting pre-donation testing was the dominant strategy. Conclusion: The selection of ALT testing strategies is a complex process influenced by multiple factors, and it is necessary to adopt an appropriate ALT screening strategy based on specific testing circumstances.

Objective: To investigate the current status and influencing factors for cognitive frailty among the elderly in community, so as to provide the evidence for early identification and prevention of cognitive frailty among the elderly. Methods: Residents aged 60 years and above with local household registration from 11 counties (cities, districts) in Zhejiang Province from 2021 to 2023 were selected as study participants using a multistage random sampling method. Demographic information, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were assessed using the Patient Health Questionnaire. Cognitive frailty was evaluated using the FRAIL Scale and the Mini-Mental State Examination. Factors affecting cognitive frailty among the elderly in community were identified using a multivariable logistic regression model. Results: A total of 16 613 individuals were surveyed, including 7 465 males (44.93%) and 9 148 females (55.07%). The average age was (70.97±7.29) years. A total of 784 individuals were detected with depressive symptoms, with a detection rate of 4.72%. A total of 724 individuals were detected with cognitive frailty, with a detection rate of 4.36%. Multivariable logistic regression analysis showed that females (OR=1.419, 95%CI: 1.179-1.708), aged ≥70 years (70-<80 years old, OR=1.869, 95%CI: 1.490-2.345; ≥80 years old, OR=5.017, 95%CI: 3.935-6.398), without a spouse (OR=1.495, 95%CI: 1.234-1.810), sedentary (OR=2.420, 95%CI: 1.829-3.202), chronic diseases (1 type, OR=1.456, 95%CI: 1.175-1.804; ≥2 types, OR=1.639, 95%CI: 1.314-2.045), and depressive symptoms (OR=4.191, 95%CI: 3.361-5.225) were associated with a higher risk of cognitive frailty among the elderly in community. Conversely, a lower risk of cognitive frailty was seen among the elderly in community who had primary school or above (primary school, OR=0.512, 95%CI: 0.389-0.676; junior high school or above, OR=0.464, 95%CI: 0.354-0.608), engaged in physical exercise (OR=0.396, 95%CI: 0.291-0.539), and were reported average or good self-rated health status (average, OR=0.641, 95%CI: 0.475-0.866; good, OR=0.150, 95%CI: 0.109-0.208). Conclusions The detection rate of cognitive frailty among the elderly in community is relatively low and is influenced by demographic factors such as gender, age, education level, as well as lifestyle like sedentary and physical exercise, and health status. It is recommended to reduce the risk of cognitive frailty among the elderly through multidimensional interventions, including health education, promotion of healthy lifestyles, and enhanced mental health support.

Objective: To establish a nomogram prediction model for Alzheimer's disease (AD) among the elderly in community, so as to provide the evidence for early screening and prevention of AD. Methods: Based on the Zhejiang Healthy Aging Cohort Study, the elderly aged 60-90 years who completed the baseline survey were selected as the study subjects. Follow-up surveys were conducted from 2015 to 2016 and from 2019 to 2021. Sociodemographic characteristics, lifestyle factors, medical history, and waist circumference were collected through questionnaire surveys and physical examinations. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a diagnosis of AD was made based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale and medical history. The participants were randomly divided into training and validation sets at 8∶2 ratio. LASSO regression was used to screen for predictive factors. Multivariable logistic regression model was used to analyze predictive factors and construct a nomogram. The model was analyzed and evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 6 988 elderly were included at baseline, with a mean age of (68.19±6.63) years. There were 3 438 males (49.20%), and 3 550 females (50.80%). The median follow-up duration was 4.90 (interquartile range, 3.80) years, with 817 new cases of AD were identified, yielding an incidence of 11.69%. LASSO regression and multivariable logistic regression showed that age (OR=1.017, 95%CI: 1.005-1.030), gender (female, OR=1.820, 95%CI: 1.533-2.165), educational level (primary school, OR=0.813, 95%CI: 0.673-0.980), physical exercise (not active, OR=1.572, 95%CI: 1.260-1.980), dining companions (spouse and children, OR=0.771, 95%CI: 0.598-0.995), baseline MMSE score (OR=0.843, 95%CI: 0.821-0.866), and waist circumference (OR=0.981, 95%CI: 0.973-0.989) were risk predictors for AD among the elderly in community. The prediction model demonstrated an area under the ROC curve of 0.740 (95%CI: 0.698-0.783) in the validation set, with a sensitivity of 0.731 and a specificity of 0.667. DCA indicated that when the probability threshold was 0.060 to 0.325, the clinical net benefit was relatively high. Conclusion The AD risk prediction model constructed in this study has good discrimination and clinical practicability, can be used for early screening of AD among the elderly in the community.

Cold has been a long-term survival challenge in the evolutionary process of mammals. In response to cold stress, in addition to brown adipose tissue (BAT) dissipating energy as heat through glucose and lipid oxidation to maintain body temperature, cold stimulation can strongly activate thermogenesis and energy expenditure in beige fat cells, which are widely distributed in the subcutaneous layer. However, the effects of cold stimulation on other tissues and systemic lipid metabolism remain unclear. Our previous research indicated that, under cold stress, BAT not only produces heat but also secretes numerous exosomes to mediate BAT-liver crosstalk. Whether subcutaneous fat has a similar mechanism is still unknown. Therefore, this study aimed to investigate the alterations in lipid metabolism across various tissues under cold exposure and to explore whether subcutaneous fat regulates systemic glucose and lipid metabolism via exosomes, thereby elucidating the regulatory mechanisms of lipid metabolism homeostasis under physiological stress. RT-qPCR, Western blot, and H&E staining methods were used to investigate the physiological changes in lipid metabolism in the serum, liver, epididymal white adipose tissue, and subcutaneous fat of mice under cold stimulation. The results revealed that cold exposure significantly enhanced the thermogenic activity of subcutaneous adipose tissue and markedly increased exosome secretion. These exosomes were efficiently taken up by hepatocytes, where they profoundly influenced hepatic lipid metabolism, as evidenced by alterations in the expression levels of key genes involved in lipid synthesis and catabolism pathways. This study has unveiled a novel mechanism by which subcutaneous fat regulates lipid metabolism through exosome secretion under cold stimulation, providing new insights into the systemic regulatory role of beige adipocytes under cold stress and offering a theoretical basis for the development of new therapeutic strategies for obesity and metabolic diseases.
Animals ; Lipid Metabolism/physiology* ; Mice ; Exosomes/metabolism* ; Cold Temperature ; Subcutaneous Fat/physiology* ; Thermogenesis/physiology* ; Adipose Tissue, Brown/metabolism* ; Male

Syringa pinnatifolia, belonging to the family Oleaceae, is a species endemic to China. It is predominantly distributed in the Helan Mountains region of Inner Mongolia and Ningxia of China. The peeled roots, stems, and thick branches have been used as a distinctive Mongolian medicinal material known as "Shan-chen-xiang", which has effects such as suppressing "khii", clearing heat, and relieving pain and is employed for the treatment of cardiovascular and pulmonary diseases and joint pain. Over the past five years, significant increase was achieved in research on chemical constituents and pharmacological effects. There were a total of 130 new constituents reported, covering sesquiterpenoids, lignans, and alkaloids. Its effects of anti-myocardial ischemia, anti-cerebral ischemia/reperfusion, sedation, and analgesia were revealed, and the mechanisms of agarwood formation were also investigated. To better understand its medical value and potential of clinical application, this review updates the research progress in recent five years focusing on the chemical constituents and pharmacological effects of S. pinnatifolia, providing reference for subsequent research on active ingredient and support for its innovative application in modern medicine system.
Medicine, Mongolian Traditional ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Syringa/chemistry*

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence