Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Dialectical behavior therapy （DBT） is one of the empirically supported therapeutic approaches for borderline personality disorder （BPD）. By integrating behaviorism， dialectical philosophy， biosocial theory and Zen principles， DBT aims to enhance patients' emotional regulation， interpersonal effectiveness and distress tolerance， thereby alleviating BPD symptoms. This article systematically reviews the theoretical foundations of DBT and its research progress in BPD treatment， to Delve into the intervention effects of DBT， as well as the adjuvant role of pharmacotherapy and physiotherapy in enhancing DBT for BPD， and analyzes the challenges faced in DBT research and clinical application. The findings are expected to provide new insights for the localization and theoretical research of DBT in China.

As a new type of high-risk packaging container, prefilled syringes are more widely used, and concerns regarding their effectiveness, stability and safety in clinical use have become prominent increasingly. However, the leachable substances from prefilled syringes may cause harm to humans in different degrees. Therefore, this paper reviews the research progress of leachable substances in prefilled syringes, which is not only of great significance for the quality control of prefilled syringe products, but also contributes to the healthy development of the industry.
Syringes ; Drug Packaging ; Quality Control

ObjectiveTo explore the main mechanism of self-precipitation formed during the decoction of Sinitang（SNT）， and to provide a research basis for exploring the differences in the toxic and effective components of this compound. MethodsThe average precipitation yields of SNT， Glycyrrhizae Radix et Rhizoma（GRR）-Aconiti Lateralis Radix Praeparata（ALRP） decoction（GF）， ALRP-Zingiberis Rhizoma（ZR） decoction（FJ）， GRR-ZR decoction（GJD）， ALRP decoction（FZ）， ZR decoction（GJ） and GRR decoction（GC） were determined. The four main self-precipitation samples of SNT， GF， FZ and GC were physically characterized by particle size， scanning electron microscopy（SEM）， pH， total dissolved solids（TDS）， conductivity， and Fourier transform infrared spectroscopy（FT-IR） analysis. The chemical compositions of SNT decoction and its different phases was identified by ultra-performance liquid chromatography-quadrupole-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-Q-Exactive Orbitrap-MS） for SNT， SNT self-precipitation and SNT supernatant， and the contents of its main toxic and effective components were determined by high performance liquid chromatography（HPLC）. ResultsPrecipitation yield results of the 7 samples of SNT decoction and single decoction showed that SNT had the highest self-precipitation yield. The formation of SNT self-precipitation was mainly related to the reaction between ALRP and GRR components to form complexes， and FT-IR showed that GRR had the greatest influence on the formation of self-precipitation. A total of 110 components were identified in the SNT decoction， including 100 components in the SNT self-precipitation and 106 components in the SNT supernatant. And quantitative results of the main toxic and effective components revealed that the reaction between ALRP and GRR components formed complexes， resulting in the following content hierarchy for free components：SNT decoctionsupernatantself-precipitation， these components included free liquiritin， benzoylmesaconine， benzoylaconitine， benzoylhypacoitine， liquiritigenin， aconitine， hypoaconitine， isoliquiritigenin and ammonium glycyrrhizinate. ConclusionSNT exhibits spontaneous precipitation during compound decoction， with GRR exerting the greatest influence on its formation. This suggests GRR plays a significant role in the detoxification of SNT. The differences in the self-precipitated toxic-effective components of SNT compound decoction primarily manifest as changes in component content， reflecting the characteristics of SNT "deposition in vitro and sustained release in vivo" and the importance of "administered at draught" in the clinical application of SNT.

BACKGROUND: T-cell lymphoblastic lymphoma/acute lymphoblastic leukemia (T-LBL/ALL) is an aggressive form of hematological malignancy associated with poor prognosis in adult patients. Histone deacetylases (HDACs) are aberrantly expressed in T-LBL/ALL and are considered potential therapeutic targets. Here, we investigated the antitumor effect of a novel HDAC inhibitor, chidamide, on T-LBL/ALL. METHODS: HDAC1, HDAC2 and HDAC3 levels in T-LBL/ALL cell lines and patient samples were compared with those in normal controls. Flow cytometry, transmission electron microscopy, and lactate dehydrogenase release assays were conducted in Jurkat and MOLT-4 cells to assess apoptosis and pyroptosis. A specific forkhead box O1 (FOXO1) inhibitor was used to rescue pyroptosis and upregulated gasdermin E (GSDME) expression caused by chidamide treatment. The role of the FOXO1 transcription factor was evaluated by dual-luciferase reporter and chromatin immunoprecipitation assays. The efficacy of chidamide in vivo was evaluated in a xenograft mouse. RESULTS: The expression of HDAC1, HDAC2 and HDAC3 was significantly upregulated in T-LBL/ALL. Cell viability was obviously inhibited after chidamide treatment. Pyroptosis, characterized by cell swelling, pore formation on the plasma membrane and lactate dehydrogenase leakage, was identified as a new mechanism of chidamide treatment. Chidamide triggered pyroptosis through caspase 3 activation and GSDME transcriptional upregulation. Chromatin immunoprecipitation assays confirmed that chidamide led to the increased transcription of GSDME through a more relaxed chromatin structure at the promoter and the upregulation of FOXO1 expression. Moreover, we identified the therapeutic effect of chidamide in vivo . CONCLUSIONS This study suggested that chidamide exerts an antitumor effect on T-LBL/ALL and promotes a more inflammatory form of cell death via the FOXO1/GSDME axis, which provides a novel choice of targeted therapy for patients with T-LBL/ALL.
Humans ; Pyroptosis/drug effects* ; Forkhead Box Protein O1/genetics* ; Aminopyridines/pharmacology* ; Animals ; Mice ; Benzamides/pharmacology* ; Cell Line, Tumor ; Precursor T-Cell Lymphoblastic Leukemia-Lymphoma/drug therapy* ; Phosphate-Binding Proteins/metabolism* ; Histone Deacetylase Inhibitors/pharmacology* ; Jurkat Cells ; Histone Deacetylases/metabolism* ; Apoptosis/drug effects* ; Gasdermins

Collagen-based materials, renowned for their biocompatibility and minimal immunogenicity, serve as exemplary substrates in a myriad of biomedical applications. Collagen-based micro/nanogels, in particular, are valued for their increased surface area, tunable degradation rates, and ability to facilitate targeted drug delivery, making them instrumental in advanced therapeutics and tissue engineering endeavors. Although extensive reviews on micro/nanogels exist, they tend to cover a wide range of biomaterials and lack a specific focus on collagen-based materials. The current review offers an in-depth look into the manufacturing technologies, drug release mechanisms, and biomedical applications of collagen-based micro/nanogels to address this gap. First, we provide an overview of the synthetic strategies that allow the precise control of the size, shape, and mechanical strength of these collagen-based micro/nanogels by controlling the degree of cross-linking of the materials. These properties are crucial for their performance in biomedical applications. We then highlight the environmental responsiveness of these collagen-based micro/nanogels, particularly their sensitivity to enzymes and pH, which enables controlled drug release under various pathological conditions. The discussion then expands to include their applications in cancer therapy, antimicrobial treatments, bone tissue repair, and imaging diagnosis, emphasizing their versatility and potential in these critical areas. The challenges and future perspectives of collagen-based micro/nanogels in the field are discussed at the end of the review, with an emphasis on the translation to clinical practice. This comprehensive review serves as a valuable resource for researchers, clinicians, and scientists alike, providing insights into the current state and future directions of collagen-based micro/nanogel research and development.
Collagen/chemistry* ; Drug Delivery Systems/methods* ; Humans ; Tissue Engineering/methods* ; Animals ; Biocompatible Materials/chemistry*

[Objective] To validate and optimize the platelet transfusion refractoriness (PTR) prediction model for patients with hematological disorders established by our center. [Methods] The data of patients with hematological diseases who received platelet transfusions from December 2021 to December 2022 were used as the training set, and data from January 2023 to December 2023 as the validation set. The validation set data was used to validate the predictive model constructed on the training set. Relevant risk factors for PTR were collected through literature review and preliminary studies。 The patients were divided into effective and ineffective groups according to the corrected count increment (CCI) of platelet counts. Predictive factors were screened using univariate and multivariate logistic regression. The calibration of the model were assessed via calibration curves, while discrimination, accuracy, sensitivity, and specificity were evaluated using receiver operating characteristic (ROC) curves Clinical utility was further analyzed with decision curve analysis (DCA). [Results] The Hosmer-Lemeshow (H-L) goodness-of-fit test for the validation set yielded S: P=0.000, indicating that the original model needs optimization. Baseline comparisons and logistic regression identified the number of red blood cell units (RBCU) and platelet units (PLT-U) transfused as key predictors for the optimized model. The H-L goodness-of-fit test S: P values for the training and validation sets were 0.930 and 0.056, respectively; the ROC areas were 0.793 5 and 0.809 4, specificities 90.95% and 84.21%, sensitivities 59.26% and 70.04%, and accuracies 78.14% and 74.10%, respectively. DCA demonstrated clinical net benefit within a prediction probability threshold range of 0.2-0.8. [Conclusion] Transfusion volumes of RBC-U and PLT-U were inversely associated with PTR in hematological patients. The resulting PTR prediction model exhibits moderate predictive efficacy and clinical benefit.

Objective To develop the evidence base for the"Guidelines for the Diagnosis and Treatment of Integrated Traditional Chinese and Western Medicine for Female Menopausal Syndrome"(hereafter referred to as"the guideline"),aiming to provide effective evidence to support the formulation of recommendation statements.Methods Based on Internationally recognized methods and standards for evidence-based guideline development,the PICO(population,intervention,comparison,outcome)framework was used to construct clinical questions.A systematic literature search was conducted across domestic and international databases to identify studies published before 30 July,2022,focusing on the treatment of menopausal syndrome with integrated Chinese and Western medicine.Studies were se-lected based on predefined inclusion and exclusion criteria.Data extraction and synthesis were performed using ReviewManager 5.3software,and the quality of evidence was assessed employing the GRADE(Grading of Recommendations,Assessment,Development and Evaluation)methodology.Results A total of 143322 articles were retrieved,with 92studies ultimately included.These studies encom-passed treatment interventions such as classical formulas,Chinese patent medicines,and distinctive therapies.The evidence from these studies was synthesized and graded,resulting in an overview of evidence pertinent to both foundational and clinical questions.Conclusion The construction process of the evidence body in this guideline is an exploratory practice of evidence-based traditional Chinese medicine.This article reviews the important steps involved,with a view to providing a reference for the development/revision of clinical practice guidelines for traditional Chinese medicine.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.