ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

ObjectiveTo characterize the evidence distribution and methodological quality of randomized controlled trials （RCTs） on oral Chinese herbal medicine （CHM） for atopic dermatitis （AD） based on evidence mapping. MethodsSeven databases （CNKI， Wanfang Data， VIP， CBM， Cochrane Library， PubMed， and Embase） and the Chinese Clinical Trial Registry were searched for the RCTs in Chinese and English. Evidence distribution was presented graphically and textually， and methodological quality was assessed via the Cochrane Risk of Bias tool （ROB 1.0）. ResultsA total of 168 RCTs were included. The number of annual publications showing an increasing trend， and 72.6% RCTs had sample sizes of 51-100 participants. The studies evaluated 108 distinct CHM interventions categorized as decoctions， granules， Chinese patent medicines， and extracts. Compound Glycyrrhizin was the most frequently used， followed by Xiaofengsan and Chushi Weiling decoction. Among the RCTs， 57.1% had the treatment courses of 4-8 weeks. Outcome measures predominantly focused on clinical response rate， skin lesion severity scores， and adverse events， with less attention to TCM symptom scores， skin barrier function， and relapse rates. The overall risk of bias was generally high. ConclusionWhile CHM for AD is a research hotspot and demonstrates clinical advantages， the related studies have problems such as unclear clinical positioning， poor research standardization and methodological quality， and insufficient prominence of TCM clinical advantages. Large-sample， methodologically rigorous， and high-quality studies are needed to enhance the evidence base for CHM in treating AD.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

The catalytic activity of 3-mercaptopyruvate (3MP) sulfurtransferase (MPST) converts 3MP to hydrogen sulfide (H₂S). However, the regulatory mechanisms governing MPST and its impact on the brain remain largely unexplored. Our study reveals the neuroprotective role of endothelial MPST-generated H₂S, regulated by protein phosphatase 2A (PP2A). Bioinformatics analysis and RNA sequencing demonstrated that endothelial PP2A is associated with neurodegenerative disease pathways. Cerebral ischemic mice exhibited significant inactivation of endothelial PP2A, evidenced by the reduction of PP2Acα in the brain endothelium. Mice with endothelium-specific null PP2A (PP2A^EC-cKO) exhibited neuronal loss, cognitive dysfunction, and long-term potentiation deficits. Postnatal inactivation of endothelial PP2A also contributes to cognitive dysfunction and neuronal loss. However, regaining endothelial PP2A activity by overexpressing Ppp2ca rescued neuronal dysfunction. Mechanistically, PP2A deficiency is intricately linked to the MPST-H₂S signaling pathway. A robust reduction in endothelial MPST-dependent H₂S production followed PP2A deficiency. Exogenous H₂S treatment and AAV-mediated overexpression of MPST in brain endothelial cells significantly mitigated neuronal dysfunction in PP2A^EC-cKO mice. Furthermore, PP2A deficiency promotes an increase in calcium influx and calpain2 phosphorylation, subsequently leading to MPST degradation. The PP2A activator (FTY720) and MPST activator (3MP sodium) both remarkably restored endothelial MPST-dependent H₂S production, subsequently rescuing ischemia-induced neurological deficits. In conclusion, our study demonstrates that endothelial PP2A deficiency leads to MPST degradation by activating calpain2, thus damaging neuronal function.

Objective To observe the application effect of N-acetylcysteine in children with respiratory tract infection.Methods According to random number table method,children with respiratory tract infection were divided into control group and treatment group.The control group was given intravenous injection of ceftazidime(30-100 mg·kg-1,q12 h)on basis of routine symptomatic treatment,while treatment group was given aerosol inhalation of N-acetylcysteine solution(0.3 g∶3 mL,qd)on basis of control group.All patients were treated for 7 d.The clinical curative effect,remission time of symptoms,changes of chest X-ray,lung function[forced expiratory volume in 1 second(FEV1),tidal volume(VT),peak expiratory flow(PEF)],serum inflammatory factors,immune function and adverse drug reactions in the two groups were compared.Results In the trial,there were 28 cases excluded due to shedding and loss of follow-up,and there were 40 cases in treatment group and 52 cases in control group,respectively.After treatment,total clinical response rates in treatment group and control group were 92.50％(37 cases/40 cases)and 76.92％(40 cases/52 cases),the difference was statistically significant(P＜0.05).After treatment,disappearance time of fever in treatment group and control group were(2.96±0.65)and(4.83±0.81)d;remission time of cough were(5.58±1.08)and(7.45±1.24)d;remission time of asthma were(3.23±0.54)and(4.72±0.75)d;disappearance time of lung rales were(4.66±0.72)and(5.94±0.87)d;FEV1 were(2.26±0.25)and(1.79±0.21)L;VT were(13.76±1.32)and(10.27±1.17)mL·kg-1;PEF were(5.78±0.68)％and(4.92±0.62)％;levels of serum C-reactive protein(CRP)were(7.68±1.18)and(9.41±1.29)mg·L-1;levels of interleukin-6(IL-6)were(18.76±3.24)and(22.75±3.85)ng·mL-1;levels of tumor necrosis factor α(TNF-α)were(8.93±1.51)and(15.46±2.24)ng·mL-1;CD4+/CD8+were 1.35±0.29 and 1.20±0.30.There were statistically significant differences in the above indexes between the treatment group and the control group(all P＜0.05).In treatment group,there were 3 cases with nausea,1 case with vomiting and 1 case with diarrhea.In control group,there were 3 cases with vomiting and 2 cases with diarrhea.There was no significant difference in incidence of adverse drug reactions between treatment group and control group[12.50％(5 cases/40 cases)vs 9.62％(5 cases/52 cases),P＞0.05].Conclusion Curative effect of N-acetylcysteine combined with ceftazidime is significant in children with respiratory tract infection,which can effectively improve lung function,relieve airway inflammation and enhance immune function,with good safety.

Objective:To analyze the clinical efficacy of electrolysis of depigmented hair using a trichiasis electrolyzer combined with hair follicle transplantation in the treatment of vitiligo-associated leukotrichia.Methods:Clinical data were retrospectively collected from 25 patients with stable vitiligo-associated leukotrichia in the Department of Dermatologic Surgery, Hangzhou Third People′s Hospital from January 2019 to January 2021. All the patients received electrolysis of depigmented hair using a trichiasis electrolyzer combined with hair follicle transplantation. Outpatient follow-up visits were conducted in the first week, as well as the first, third and sixth months after surgery. The texture and growth status of transplanted hair were observed, and the survival rate of transplanted hair follicles and the proportion of newborn white hair in white hair in the original lesions were recorded.Results:Among the 25 patients with stable vitiligo, there were 14 males and 11 females, and their disease duration ranged from 2 to 15 years, with the average duration being 5.8 years. A total of 30 white patches accompanied by leukotrichia were included, including 9 on the scalp, 7 on the eyebrows and 14 on the eyelashes. One week after surgery, the transplanted hair survived well in all patients, without obvious shedding or local infection. Six months after surgery, repigmentation was observed in most hair in the original lesion area, and only a small amount of white hair grew out, without obvious scarring; the survival rate of transplanted hair follicles was 76.5% ± 10.0%, and the proportion of newborn white hair in white hair in the original lesions was 16.7% ± 7.8%.Conclusion:Electrolysis of depigmented hair using a trichiasis electrolyzer combined with hair follicle transplantation was effective in the treatment of vitiligo-associated leukotrichia, with a simple treatment process and few postoperative complications, which provided a reliable choice for the clinical treatment of vitiligo-associated leukotrichia.

Objective: To find a viable alternative to reduce the number of doses required for the patients with post-traumatic stress disorder (PTSD), and to improve efficacy and patient compliance. Methods: In this study, we used ginger oil, a phytochemical with potential therapeutic properties, to prepare ginger oil patches. High-performance liquid chromatography (HPLC) was used to quantify the main active component of ginger oil, 6-gingerol. Transdermal absorption experiments were conducted to optimize the various pressure-sensitive adhesives and permeation enhancers, including their type and concentration. Subsequently, the ginger oil patches were optimized and subjected to content determination and property evaluations. A PTSD mouse model was established using the foot-shock method. The therapeutic effect of ginger oil patches on PTSD was assessed through pathological sections, behavioral tests, and the evaluation of biomarkers such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), brain-derived neurotrophic factor (BDNF), and melatonin (MT). Results: The results demonstrated that ginger oil patches exerted therapeutic effects against PTSD by inhibiting inflammatory responses and modulating MT and BDNF levels. Pharmacokinetic experiments revealed that ginger oil patches maintained a stable blood drug concentration for at least one day, addressing the rapid metabolism drawback of 6-gingerol and enhancing its therapeutic efficacy. Conclusions Ginger oil can be prepared as a transdermal drug patch that meets these requirements, and the bioavailability of the prepared patch is better than that of oral administration. It can improve PTSD with good patient compliance and ease of administration. Therefore, it is a promising therapeutic formulation for the treatment of PTSD.