This article systematically analyzes the historical evolution of the name， origin， quality evaluation， harvesting， processing and other aspects of Picrorhizae Rhizoma by referring to the medical books， prescription books， and other documents of the past dynasties， combined with relevant modern research materials， in order to provide a basis for the development and utilization of famous classical formulas containing this medicinal herb. The research results indicate that Picrorhizae Rhizoma was first recorded in New Revised Materia Medica from the Tang dynasty. Throughout history， Huhuanglian has been used as its official name， and there are also aliases such as Gehu Luze， Jiahuanglian and Hulian. The main source of past dynasties is the the rhizomes of Picrorhiza kurrooa and P. scrophulariiflora. In ancient times， Picrorhizae Rhizoma was mainly imported by foreign traders via Guangzhou and other regions， and also produced in China， mainly in Xizang. In ancient times， it was harvested and dried in early August of the lunar calendar， while in modern times， it is mostly harvested from July to September， with the best quality being those with thick and crispy rhizomes without impurities， and bitter taste. Throughout history， Picrorhizae Rhizoma was collected， washed， sliced， and dried before being used as a raw material for medicine， it has a bitter and cold taste， mainly used to treat bone steaming， hot flashes， infantile chancre fever， and dysentery. There is no significant difference in taste and efficacy between ancient and modern times. Based on the research results， it is recommended that the rhizomes of P. scrophulariiflora in the 2020 edition of Chinese Pharmacopoeia， or the rhizomes of P. kurrooa， can be used in famous classical formulas containing this medicinal herb， which can be processed according to the processing requirements marked by the original formula. For those without clear processing requirements， the dried raw products are used as medicine.

OBJECTIVE: To compare clinical effect of arthroscopic double row fixation and single row fixation in treating Ideberg typeⅠA scapular glenoid fracture. METHODS: From June 2018 to December 2022, 26 patients with Ideberg typeⅠA scapular glenoid fracture treated with shoulder arthroscopy were divided into single-row anchor group and double-row anchor group according to the fixation method of fracture block. There were 12 patients in single-row anchor group, including 7 males and 5 females, aged from 25 to 53 years old with an average of (38.42±9.61) years old;the time from injury to operation ranged from 2 to 7 days with an average of (4.75±1.82) days. There were 14 patients in double-row anchor group, including 10 males and 4 females, aged from 21to 53 years old with an average of (37.36±10.19) years old;the time from injury to operation ranged from 1 to 8 days with an average of (4.21±2.01) days. The changes of shoulder joint flexion, abduction, lateral lateral rotation, Constant-Murley shoulder function score and Rowe scores were compared between two groups before operation and 1 year after operation. The percentage of bone mass in pelvis area before operation and the percentage of bone defect in pelvis area at the latest follow-up were compared between two groups. RESULTS: All patients were followed up for 12 to 15 months with an average of (13.08±1.17) months in single-row anchor group and 12 to 15 months with an average of (13.29±1.07) months in double-row anchor group, with no statistical significance between two groups (P>0.05). The results of anterior flexion, abduction and lateral lateral rotation in single-row anchor group were(86.67±6.62) °, (79.50±5.68) °, (38.17±1.70) ° before operation, and (162.50±4.52)°, (169.17±3.35)°, (50.67±10.20)° at 1 year after operation; while in double-row anchor group were (84.14±5.48) °, (81.71±5.20) °, (39.29±3.63) ° before operation and (162.29 ± 5.53) °, (167.14±3.61) °, (56.93±9.56) ° at 1 year after operation;the difference between two groups before operation and 1 year after operation was statistically significant (P<0.05). There were no significant difference between two groups (P>0.05). Constant-Murley scores and Rowe scores in single-row anchor group were (55.42±3.75), (43.75±18.49) before operation and (94.83±2.21), (95.42±4.50) at 1 year after operation, respectively;while in double-row anchor group were (54.50±7.88), (41.79±18.25) before operation and (94.36±4.73), (95.00±4.80) at 1 year after operation;there was no significant difference in Constant-Murley score and Rowe score between two groups before operation and 1 year after operation (P>0.05). There was significant difference in the percentage of bone mass in pelvis area between two groups before operation (P>0.05). There was no significant difference in the percentage of bone defect in the shoulder area between single-row anchor group(4.42±1.51)% and double-row anchor group (2.71±1.44)% at 1 year after operation (P<0.05). CONCLUSION Both single and double row fixation techniques for the treatment of Ideberg typeⅠA scapular glenoid fracture could receive satisfactory functional recovery. However, double-row fixation has more advantages in reducing bone resorption of fracture mass.
Humans ; Female ; Male ; Middle Aged ; Arthroscopy/methods* ; Adult ; Scapula/surgery* ; Case-Control Studies ; Fractures, Bone/physiopathology* ; Fracture Fixation, Internal/methods* ; Shoulder Joint/physiopathology* ; Range of Motion, Articular

Neural oscillatory changes play a critical role in pain and analgesia research. Previous studies on pain-related neural oscillations have primarily utilized electroencephalogram (EEG) power spectral analysis, revealing a strong correlation between alpha ( α) power and subjective pain perception. However, alpha power may be influenced by the baseline of the power spectrum, making it difficult to accurately capture the true changes in alpha oscillations. This study employed power spectral analysis and further applied a power spectral parameterization method, which decomposed the power spectrum into periodic and aperiodic components, to compare EEG α power in 50 primiparous women who underwent severe pain during the first stage of labor before and after epidural analgesia. The results indicated no significant differences in α power between pre- and post-analgesia conditions. However, following power spectral parameterization, the aperiodic component of the EEG significantly decreased after analgesia, whereas the periodic component of α power showed a significant increase. This study not only validates the effectiveness and validity of the power spectral parameterization method in analgesia research but also uncovers the differential regulatory mechanism by which analgesia modulates the periodic and aperiodic components of α oscillations.
Humans ; Electroencephalography/methods* ; Female ; Adult ; Alpha Rhythm ; Pregnancy ; Young Adult ; Analgesia, Epidural

The aqueous two-phase system (ATPS) is an all-aqueous system fabricated from two immiscible aqueous phases. It is spontaneously assembled through physical liquid-liquid phase separation (LLPS) and can create suitable templates like the multicompartment of the intracellular environment. Delicate structures containing multiple compartments make it possible to endow materials with advanced functions. Due to the properties of ATPSs, ATPS-based drug delivery systems exhibit excellent biocompatibility, extraordinary loading efficiency, and intelligently controlled content release, which are particularly advantageous for delivering drugs in vivo. Therefore, we will systematically review and evaluate ATPSs as an ideal drug delivery system. Based on the basic mechanisms and influencing factors in forming ATPSs, the transformation of ATPSs into valuable biomaterials is described. Afterward, we concentrate on the most recent cutting-edge research on ATPS-based delivery systems. Finally, the potential for further collaborations between ATPS-based drug-carrying biomaterials and disease diagnosis and treatment is also explored.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Traditional evidence-based medicine has been essential in medical practice and health decision-making. However, it has also continuously exposed shortcomings such as low efficiency in evidence generation, narrow scope of coverage, and imperfect integration strategies, making it challenging to serve clinical diagnosis and treatment and regulatory decision-making. Therefore, it is urgent to adapt to the development of cutting-edge technology and to expand and improve the concept of evidence-based medicine. Deep evidence-based medicine proposed in 2023 aims to advocate the innovative use of the latest artificial intelligence and natural language processing technologies, comprehensively expanding the breadth, depth, and integrability of evidence and improving the efficiency of evidence generation and integration. Building a new generation of evidence-based decision-making ecosystems based on deep evidence-based medicine has broad prospects for practical application. It can promote the development of evidence retrieval, generation, integration, dissemination, transformation, and application, deeply explore imaging, multi-omics, and real-world data to increase the utilization potential of real-world evidence, establish dynamic literature management platforms and decision support tools, reduce resource waste, and promote evidence flow. Utilizing this system can help obtain individual-centered comprehensive clinical evidence and play a significant role in talent training, reforming evidence-based teaching, popularizing science, and ultimately promoting the goal of "One Health".

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

Objective:Diabetic retinopathy (DR) has been reported as the leading cause of blindness among diabetic adults, which is closely related to poor quality of life and increased burden of disability. This study aimed to aggregate the optimally available evidence on modifiable risks of DR.Methods:Until June 2023, PubMed, Cochrane Library, CNKI, and Wanfang databases were used to retrieve Meta-analysis about various risk factors for DR, and Meta-analysis were analyzed and summarized. R 4.3.2 software was used for each Meta-analytic association to calculate the effect size, 95% CI, heterogeneity, small-study effects, excess significance bias, and 95% prediction intervals. The credibility of significant evidence was graded. Results:We captured 23 eligible papers (72 associations) covering a wide range of medication use, concomitant diseases, daily intervention, biomarkers, lifestyle, and physical measurement index. Among them, higher HbA1c variability ( RR=1.45, 95% CI: 1.26-1.66) and urine microalbumin positive ( OR=2.44, 95% CI: 1.99-2.97) were convincing (grade Ⅰ) evidence, and insulin use ( RR=3.48, 95% CI: 2.14-5.67) was highly suggestive (grade Ⅱ) evidence. Moreover, hypertension ( OR=2.03, 95% CI: 1.06-3.97), poor glycemic control ( OR=4.35, 95% CI: 1.47-12.85), positive macroalbuminuria ( OR=8.42, 95% CI: 3.52-20.15), long sleep duration ( OR=2.05, 95% CI: 1.37-3.05), vitamin D deficiency ( OR=2.02, 95% CI: 1.17-3.50), periodontitis ( OR=4.51, 95% CI: 1.76-11.55) were the main risk factors for DR. Intensive blood pressure intervention ( RR=0.78, 95% CI: 0.65-0.94), dietary control ( OR=0.64, 95% CI: 0.47-0.89) and moderate intensity physical activity ( RR=0.76, 95% CI: 0.59-0.97) yielded significant protective associations with DR. Conclusions:Intensive blood pressure glycemic control, and a healthy lifestyle pattern could reduce the risk of DR. This study provides the evidence to identify high-risk populations and recommends rational treatment options and healthy living interventions.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.