Fecal microbiota transplantation (FMT) technology originated in China during the Eastern Jin Dynasty and has rapidly developed over the past two decades, becoming a primary method for studying the causal relationship between gut microbiota and the occurrence and progression of diseases. At the same time, the therapeutic effects of FMT in the field of gastrointestinal diseases have gained widespread recognition and are gradually expanding into other disease areas. The FMT procedure is relatively complex, and there is currently no standardized method; its success is influenced by various factors, including the donor, recipient, processing of the fecal material, and the method of implantation. Given the increasingly recognized relationship between gut microbiota and various diseases, FMT has become a research hotspot in both scientific studies and clinical applications, achieving a series of significant advancements. To help researchers better understand this technology, this paper will outline the development history of FMT, summarize common operational methods in research and clinical settings, review its application progress, and look forward to future development directions.

ObjectiveThe central symptom of Parkinson’s disease (PD) is impaired motor function. Beta-band electrical activity in the motor network of the basal ganglia is closely related to motor function. In this study, we combined scalp electroencephalography (EEG), brain functional network, and clinical scales to investigate the effects of beta burst-period neural electrical activity on brain functional network characteristics, which may serve as a reference for clinical diagnosis and treatment. MethodsThirteen PD patients were included in the PD group, and 13 healthy subjects were included in the healthy control group. Resting-state EEG data were collected from both groups, and beta burst and non-burst periods were extracted. A phase synchronization network was constructed using weighted phase lag indices, and the topological feature parameters of phase synchronization network were compared between the two groups across different periods and four frequency bands. Additionally, the correlation between changes in network characteristics and clinical symptoms was analyzed. ResultsDuring the beta burst period, the topological characteristic parameters of phase synchronization network in all four frequency bands were significantly higher in PD patients compared to healthy controls. The average clustering coefficient of the phase synchronization network in the beta band during the beta burst period was negatively correlated with UPDRS-III scores. In the low gamma band during the non-burst period, the average clustering coefficient of phase synchronization network was positively correlated with UPDRS and UPDRS-III scores, while UPDRS-III scores were positively correlated with global efficiency and average degree. ConclusionThe brain functional network features of PD patients were significantly enhanced during the beta burst period. Moreover, the beta-band brain functional network characteristics during the beta burst period were negatively correlated with clinical scale scores, whereas low gamma-band functional network features during the non-burst period were positively correlated with clinical scale scores. These findings indicate that motor function impairment in PD patients is associated with the beta burst period. This study provides valuable insights for the diagnosis of PD.

Cistanches Herba(CH) is a famous tonic traditional Chinese medicine, with the effects of tonifying kidney Yang, nourishing kidney Yin, replenishing essence and blood, and moistening the intestines to relieve constipation. Modern pharmacological studies have shown that CH has anti-aging, anti-fatigue, immunomodulatory, neuroprotective, and anti-aging activities, serving as an ideal raw material for the development of pharmaceuticals and health products. In 2023, CH was added in the catalog of medicinal and food substances, which provided policy support for its application in conventional food products and expanding pathways for industrial diversification. To comprehensively understand current development status of CH products, this review systematically investigated professional databases including Yaozhi(https://db.yaozh.com), Chinese Pharmacopoeia, Compendium of National Standards for Chinese Patent Medicines, and Kezhuang and collected market survey data to thoroughly review the applications of CH as a primary ingredient in domestic and international Chinese patent medicines, health foods, cosmetics, and common food products. Furthermore, this review points out challenges in the current industrial development and future potential market prospects, aiming to provide guidance for the development and industrialization of CH-based pharmaceuticals and health products, thereby promoting the vigorous growth of the CH industry.
Drugs, Chinese Herbal/pharmacology* ; Humans ; Cistanche/chemistry* ; Animals ; Medicine, Chinese Traditional

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Hepatocellular carcinoma （HCC） is one of the most common malignant tumors with high incidence and mortality rates worldwide， which brings a huge burden to the physical and mental health and socio-economic life of patients. The adenosine monophosphate-activated protein kinase （AMPK） signaling pathway serves as the regulatory center of cellular energy metabolism and is closely associated with the biological activities of HCC cells， including autophagy， apoptosis， and angiogenesis， and it has become a hot topic in current cancer research. Traditional Chinese medicine drugs are abundant in natural components such as flavonoids， alkaloids， and phenols and have the characteristics of multiple targets， pathways， components， and hierarchies. By targeting the AMPK signaling pathway， these components can be used alone or in combination with conventional antitumor therapies to exert an anti-tumor effect on HCC from various aspects. This article reviews and summarizes the extracts of traditional Chinese medicine that target the AMPK signaling pathway for the prevention and treatment of HCC， in order to provide a theoretical basis and a reference for the clinical application of traditional Chinese medicine in the treatment of HCC and the development of related drugs.

BACKGROUND:Bone wax is a filler that can be used for bone hemostasis.Although modification of bone wax formulations is attempted worldwide,its inertness is still the main challenge today.There is an urgent clinical need to develop novel orthopedic hemostatic materials with hemostasis,osteogenesis and antibacterial properties. OBJECTIVE:To review the development of orthopedic hemostatic materials including bone wax and its substitutes. METHODS:PubMed,Web of Science,WanFang,CNKI and VIP databases were searched for literature related to bone wax,hemostatic materials,and research progress of orthopedic hemostatic materials,and 136 articles were selected for inclusion in the review by reading the abstracts of the articles in the initial screening. RESULTS AND CONCLUSION:To replace traditional bone wax,researchers have developed various orthopedic hemostatic materials based on the needs of practical scenarios such as hemostasis and osteogenesis.However,relevant studies mostly focus on basic physical and chemical and performance tests,lack a systematic evaluation system,and lack sufficient reports of large animal experiments and clinical trials.Therefore,bone wax is still a recognized orthopedic hemostatic material at present.The fundamental reason is that the design of existing materials cannot timely meet the new needs of intraoperative hemostasis,postoperative osteogenesis and clinical practice.In the future,the structure,composition and function of existing hemostatic and osteogenic materials need to be integrated and redesigned to meet the increasing demand for hemostatic and osteogenic materials.

Objective:To explore the clinical and electrophysiological characteristics of peripheral neuropathy in prediabetic patients.Methods:Subjects aged 20-65 years with high-risk factors of impaired glycemia enrolled in Beijing Tiantan Hospital, Capital Medical University from 2019 to 2022 were recruited to conduct oral glucose tolerance test, after excluding other causes of neuropathy or radiculopathy. Patients with impaired fasting glucose or impaired glucose tolerance were defined by American Diabetes Association criteria. These patients were divided into clinical polyneuropathy (PN) and clinical non-PN groups, according to the 2010 Toronto consensus criteria and the presence of PN symptoms and signs or not. Nerve conduction studies (NCS), F wave, sympathetic skin response (SSR), R-R interval variation (RRIV) and current perception thresholds (CPT) were performed and the abnormal rate was compared between different electrodiagnostic methods and between clinical subgroups.Results:Among the 73 prediabetic patients ultimately enrolled, only 20 (27.4%) can be diagnosed as clinical PN according to the Toronto consensus criteria. The abnormal rate of CPT (68.5%, 50/73) was significantly higher than those of F wave (2.7%, 2/73), lower limb NCS (0, 0/73), upper limb NCS changes of carpal tunnel syndrome (26.0%, 19/73), SSR (6.8%, 5/73) and RRIV (5.5%, 4/73; McNemar test, all P<0.001). With sinusoid-waveform current stimuli at frequencies of 2 000 Hz, 250 Hz and 5 Hz, the CPT device was used to measure cutaneous sensory thresholds of large myelinated, small myelinated and small unmyelinated sensory fibers respectively. CPT revealed a 21.9% (16/73) abnormal rate of unmyelinated C fiber in the hands of prediabetic patients, significantly higher than that of large myelinated Aβ fibers [8.2% (6/73), χ2=5.352, P=0.021]. Both abnormal rates of small myelinated Aδ [42.5% (31/73)] and unmyelinated C fibers [39.7% (29/73)] in the feet of prediabetic patients were significantly higher than that of large myelinated Aβ fibers [11.0% (8/73), χ2=18.508, 15.965, both P<0.001]. Compared with the clinical non-PN group, the abnormal rates of CPT [90.0% (18/20) vs 60.4% (32/53), χ2=5.904, P=0.015] and SSR [20.0% (4/20) vs 1.9% (1/53), P=0.016) were significantly higher in the clinical PN group. Conclusions:Peripheral neuropathies in prediabetic patients are usually asymptomatic or subclinical, and predispose to affect unmyelinated and small myelinated sensory fibers. Selective electrodiagnostic measurements of small fibers help to detect prediabetic neuropathies in the earliest stages of the disease.

In April 2024, the World Health Organization/International Agency for Research on Cancer (IARC) published the global cancer statistics 2022 in the CA: Cancer Journal for Clinicians. This report focuses on the incidence and mortality of 36 cancers in 185 countries or territories worldwide, analyzing the differences of gender, geographic region, and the Human Development Index (HDI) level. It is estimated that in the year 2022, there were 19.96 million new cancer cases and 9.74 million cancer deaths worldwide. Lung cancer (2 480 301, 12.4%) was the most frequently diagnosed cancer in 2022, followed by female breast cancer (2 295 686, 11.5%), colorectal cancer (1 926 118, 9.6%), prostate cancer (1 466 680, 7.3%), and gastric cancer (968 350, 4.9%). Lung cancer (1 817 172, 18.7%) was also the leading cause of cancer death, followed by colorectal cancer (903 859, 9.3%), liver cancer (757 948, 7.8%), female breast cancer (665 684, 6.9%), and gastric cancer (659 853, 6.8%). With demographics-based predictions indicating that the number of new cases of cancer will reach over 35 million by 2050. The Beijing Office for Cancer Prevention and Control team has collated this report and briefly interpreted it in combination with the current situation of cancer incidence and mortality in China.