Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Acute lung injury(ALI) is a critical clinical condition primarily characterized by refractory hypoxemia and infiltration of inflammatory cells in lung tissue, which can progress into a more severe form known as acute respiratory distress syndrome(ARDS). Immune cells and inflammatory cytokines play important roles in the progression of the disease. Due to its unclear pathogenesis and the lack of effective clinical treatments, ALI is associated with a high mortality rate and severely affects patients' quality of life, making the search for effective therapeutic agents particularly urgent. Ginseng Radix et Rhizoma, the dried root of the perennial herb Panax ginseng from the Araliaceae family, contains active ingredients such as saponins and polysaccharides, which possess various pharmacological effects including anti-tumor activity, immune regulation, and metabolic modulation. In recent years, studies have shown that ginsenosides exhibit notable effects in reducing inflammation, ameliorating epithelial and endothelial cell injury, and providing anticoagulant action, indicating their comprehensive role in alleviating lung injury. This review summarizes the pathogenesis of ALI and the molecular mechanisms through which ginsenosides act at different stages of ALI development. The aim is to provide a scientific reference for the development of ginsenoside-based drugs targeting ALI, as well as a theoretical basis for the clinical application of Ginseng Radix et Rhizoma in the treatment of ALI.
Ginsenosides/pharmacology* ; Humans ; Acute Lung Injury/immunology* ; Animals ; Panax/chemistry* ; Drugs, Chinese Herbal

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Neuraxial opioids, widely used in obstetric and perioperative pain management, often lead to unwanted itch, reducing patient satisfaction. While the μ-opioid receptor has been implicated in opioid-induced itch, the genetic basis for variable itch incidence remains unknown. This study examined 3616 patients receiving epidural opioids, revealing an itch occurrence of 26.55%, with variations among opioid types and gender. Analysis of the OPRM1 gene identified six single-nucleotide polymorphisms, notably rs1799971 (A118G), that correlated with opioid-induced itch. Mouse models with an equivalent A112G mutation showed reduced neuraxial opioid-induced itch and light touch-evoked itch, mirroring human findings. The 118G allele demonstrated an anti-itch effect without impacting analgesia, addiction, or tolerance, offering insights for risk stratification and potential anti-itch pretreatment strategies.
Receptors, Opioid, mu/genetics* ; Pruritus/chemically induced* ; Humans ; Analgesics, Opioid/administration & dosage* ; Female ; Male ; Animals ; Polymorphism, Single Nucleotide/genetics* ; Adult ; Mice ; Middle Aged

Clear aligner treatment is a novel technique in current orthodontic practice. Distinct from traditional fixed orthodontic appliances, clear aligners have different material features and biomechanical characteristics and treatment efficiencies, presenting new clinical challenges. Therefore, a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique. This expert consensus discusses case selection and grading of treatment difficulty, principle of clear aligner therapy, clinical procedures and potential complications, which are crucial to the clinical success of clear aligner treatment.
Humans ; Consensus ; Orthodontic Appliance Design ; Orthodontic Appliances, Removable ; Tooth Movement Techniques/methods* ; Malocclusion/therapy* ; Orthodontics, Corrective/instrumentation*

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BackgroundThe functional changes of the default mode network （DMN） are closely related to the onset of major depressive disorders. However， the relationship between the DMN subsystem （core subsystem， dorsomedial prefrontal cortex subsystem， medial temporal lobe subsystem） and symptoms of first-episode major depressive disorder remains unclear. ObjectiveTo investigate abnormal functional connectivity between DMN subsystems and the whole brain in first-episode major depressive disorder patients during the resting-state， and to analyse the correlations between these functional connectivity patterns and clinical symptoms， so as to reveal the potential neural mechanisms from the perspective of DMN subsystem. MethodsFrom September 2020 to September 2023， a total of 64 first-episode outpatients and inpatients meeting the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders， fourth edition （DSM-IV） were enrolled at the Inner Mongolia Autonomous Region Mental Health Center as the study group. During the same period， 54 healthy volunteers matched for age， gender， and years of education were recruited from the community as the control group. Both groups were assessed using the Hamilton Depression Scale-24 item （HAMD-24）. Resting-state functional magnetic resonance images （rs-fMRI） of the two groups were acquired using a Siemens 3.0 T scanner， and differences in functional connectivity between DMN subsystems （core subsystem， dorsomedial prefrontal cortex subsystem， medial temporal lobe subsystem） and the whole brain were compared. The functional connectivity values of brain regions with statistically significant differences between the two groups were extracted. Spearman's rank correlation coefficient analysis was used to investigate the correlation between these functional connectivity values and HAMD-24 scores of the study group. ResultsUltimately， 46 patients and 43 controls completed the study. Compared with the control group， the study group exhibited significantly stronger functional connectivity in the following pathways： between the right superior parietal lobule （core subsystem） and right cerebellar lobule VIII （t=3.954， P<0.05， GRF-corrected）， between the right lateral temporal cortex （dorsomedial prefrontal cortex subsystem） and right cerebellar lobule VIII， right and left hippocampi， right medial， and paracingulate gyrus （t=4.595， 4.208， 5.200， 4.038， P<0.05， GRF-corrected）， and between the temporoparietal junction （dorsomedial prefrontal cortex subsystem） and left lingual gyrus and right cerebellar lobule VIII （t=3.557， 4.274， P<0.05， GRF-corrected）. Conversely， weaker functional connectivity was observed between the right inferior frontal gyrus and left gyrus rectus （t=-3.824， P<0.05， GRF-corrected）. Furthermore， within the study group， the functional connectivity values between the right lateral temporal cortex and right hippocampus， as well as between the temporoparietal junction and right cerebellar lobule VIII， were both negatively correlated with the HAMD-24 cognitive impairment factor score （r=-0.306， -0.318， P<0.05）. ConclusionIncreased functional connectivity between the DMN （specifically its core and dorsomedial prefrontal cortex subsystems） and cerebellum， partial limbic system， and lingual gyrus may be associated with the neuropathology of first-episode major depressive disorder. Furthermore， alterations in functional connectivity between the dorsomedial prefrontal cortex subsystem and both the cerebellum and hippocampus in these patients may be related to cognitive function. ［Funded by 2019 Annual Inner Mongolia Autonomous Region Natural Science Foundation Project （number， 2019MS03038）； 2023 Annual Inner Mongolia Autonomous Region Natural Science Foundation Project （number， 2023MS08028）］

Objective To investigate the diagnostic value of our regression model based on quantitative parameters of dual-energy CT and clinical features for stages T2 and T3 colorectal cancer.Methods A cross-section study was performed on 91 patients with colorectal cancer confirmed by postoperative pathology in our hospital from January 2022 to November 2023.All of them underwent dual-energy CT examination.According to the pathological T staging criteria of Chinese Colorectal Cancer Diagnosis and Treatment Standard(2020 Edition),they were divided into T2 group(n=43)and T3 group(n=48).Univariate analysis was used to compare the differences in quantitative CT parameters and clinical features between the 2 groups,and the obtained significant variables were employed to construct diagnosis models by univariate or multivariate logistic regression analysis.The area under receiver operating characteristic curve(AUC)of the CT parametric model and the model combined with clinical features was compared to evaluate the efficacy of diagnosing T2 and T3 stages.Results Univariate analysis showed that carcinoembryonic antigen(CEA),N stage,tumor location,tumor longest diameter(LD),CT value of virtual noncontrast(CT-VNC),fat fraction,electron density(Rho)and dual energy index(DEI)were significantly different between the T2 and T3 groups(P＜0.05).Multivariate logistic regression analysis found that N stage,tumor location,LD,fat fraction and DEI were independent risk factors for the diagnosis of stage T3.The AUC value of the model of above CT parameters in diagnosing stage T3 colorectal cancer was 0.671(95％CI:0.558～0.783),and the AUC value of the combined model of above CT parameters and clinical features was 0.886(95％CI:0.815～0.957),and statistical difference was observed in the AUC value between the combined model and the CT parametric model(P＜0.01).Conclusion The regression model constructed with dual-energy CT quantitative parameters combined with clinical features has high value in the preoperative diagnosis of stages T2 and T3 colorectal cancer before surgery.

Colorimetric analysis is a detection and quantification method based on observable color changes in response to analytes,which offers significant advantages including visually detectable signals,straightforward operation,rapid response,and low cost.Consequently,it plays a crucial role in a variety of fields.With increasingly diverse and complex application,colorimetric analysis requires continuous improvement in sensitivity,adaptability to diverse detection environments,and complex data handling capabilities.In recent years,the development of artificial intelligence technology,particularly within its core domain of machine learning(ML),has led to significant advancements in colorimetric analysis.The ML-assisted colorimetric analysis enables high-throughput and high-sensitivity detection,alongside automated analysis,thereby providing novel strategies to overcome the inherent limitations.This review categorized machine learning techniques and summarized their application in colorimetric analysis,introducing two fundamental categories of supervised learning,and unsupervised learning based on the division of core learning paradigms.The research progress of ML-assisted colorimetric analysis in the fields of environmental monitoring,biochemical detection,and food safety were summarized.Finally,the current challenges facing by this research area were analyzed and the research prospect of ML-assisted colorimetric analysis was outlined.

In 2009,the World Health Organization included snakebite on the list of neglected tropical diseases,acknowledging it as a common occupational hazard for farmers,plantation workers,and others,causing tens of thousands of deaths and chronic physical disabilities every year.This guideline aims to provide practical information to help clinical professionals evaluate and treat snakebite victims.These recommendations are based on clinical experience and clinical research evidence.This guideline focuses on the following topics:snake venom,clinical manifestations,auxiliary examination,diagnosis,treatments,and prevention.