Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Background::Differentiated thyroid cancer (DTC) is commonly diagnosed in women of child-bearing age, but whether pregnancy influences the prognosis of DTC remains controversial. This study aimed to summarize existing evidence regarding the association of pregnancy with recurrence risk in patients previously treated for DTC.Methods::We searched PubMed, Embase, Web of Science, Cochrane, and Scopus based on the prespecified protocol registered at PROSPERO (CRD42022367896). After study selection, two researchers independently extracted data from the included studies. For quantitative data synthesis, we used random-effects meta-analysis models to pool the proportion of recurrence (for pregnant women only) and odds ratio (OR; comparing the risk of recurrence between the pregnancy group and the nonpregnancy group), respectively. Then we conducted subgroup analyses to explore whether risk of recurrence differed by response to therapy status or duration of follow-up time. We also assessed quality of the included studies.Results::A total of ten studies were included. The sample size ranged from 8 to 235, with participants’ age at pregnancy or delivery ranging from 28 to 35 years. The follow-up time varied from 0.1 to 36.0 years. The pooled proportion of recurrence in all pregnant patients was 0.13 (95% confidence intervals [CI]: 0.06-0.25; I2: 0.58). Among six included studies reporting response to therapy status before pregnancy, we observed a trend for increasingly higher risk of recurrence from excellent, indeterminate, and biochemically incomplete to structurally incomplete response to therapy ( Ptrend <0.05). The pooled risk of recurrence in the pregnancy group showed no evidence of a significant difference from that in the nonpregnancy group (OR: 0.75; 95% CI: 0.45-1.23; I2: 0). The difference in follow-up time (below/above five years) was not associated with either the proportion of recurrence in all pregnant patients ( P >0.05) or the OR of recurrence in studies with a comparison group ( P >0.05). Two included studies that focused on patients with distant metastasis also did not show a significant difference in disease recurrence between pregnancy and nonpregnancy groups (OR: 0.51 [95% CI: 0.14-1.87; I2: 59%]). Conclusion::In general, pregnancy appears to have a minimal association with the disease recurrence of DTC with initial treatment. Clinicians should pay more attention to progression of DTC among pregnant women with biochemical and/or structural persistence.Registration::PROSPERO, https://www.crd.york.ac.uk/PROSPERO/; No. CRD42022367896.

Objective:To analyze the effects of highdose methotrexate(HD-MTX)and lenalidomide as central nervous system(CNS)prophylaxis strategies in patients with diffuse large B-cell lymphoma(DLBCL).Methods:The data of DLBCL patients with high risk of CNS recurrence who were initially treated in Fujian Provincial Hospital and Fujian Cancer Hospital from January 2012 to June 2022 were analyzed retrospectively.The patients were divided into HD-MTX group and lenalidomide group according to different prophylaxis strategies.Each group was further divided into high-risk group and medium-risk group based on CNS-IPI score and/or testicular involvement.The CNS relapse-free survival(CRFS)rate,adverse effects,and the effects of different prophylaxis strategies on overall survival(OS)rate and progression-free survival(PFS)rate were evaluated in different groups and subgroups.Results:There were 200 patients enrolled in this study,80 cases in lenalidomide group and 120 cases in HD-MTX group.According to the delivery timing of prophylactic HD-MTX,the patients in HD-MTX group were further divided into two groups:80 cases at the end of induction chemotherapy and 40 cases during chemotherapy interval.At a median follow-up of 48(14-133)months,the 4-year CRFS rate,4-year PFS rate,and 4-year OS rate of the HD-MTX group was 93.6％,57.2％,and 68.8％,respectively,while that of the lenalidomide group was 90.4％,69.4％and 75.6％.There were no significant differences in 4-year CRFS rate,4-year PFS rate,and 4-year OS rate between HD-MTX group and lenalidomide group(all P＞0.05),but lenalidomide group showed a trend of improvement in PFS.Further subgroup analysis showed that there was no significant difference in 4-year CRFS rate between high-risk patients of the two groups(91.7％vs 83.4％,P＞0.05),while 4-year PFS rate showed difference(49.5％vs 64.2％,P＜0.05).A total of 248 cycles were collected for adverse reaction analysis in the HD-MTX group,and 25 cycles occurred neutropenia accompanied with infection(10.1％),while in lenalidomide group 240 cycles were collected in which 20 cycles occurred neutropenia accompanied with infection(8.3％).Both the two groups had no treatment-related deaths.Conclusion:Compared with HD-MTX,lenalidomide combined with immunochemotherapy can prevent CNS relapse,at the same time,improve prognosis,which is a safe and well tolerated central prophylaxis strategy.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Objective:To analyze the safety and efficacy of ultrasound-guided percutaneous thermal ablation treatment for multiple T1N0M0 papillary thyroid carcinoma(PTC)with over 5 years follow-up.Methods:From January 2014 to January 2019,a retrospective analysis was conducted on patients with multiple T1N0M0 PTC who underwent ultrasound-guided thermal ablation.Patients with bilateral or unilateral lobes with isthmus PTC were enrolled in this study and were followed up at 1,3,6,12,24,36,48,and 60 months after ablation.The clinical data,ultrasound characteristics and ablation parameters of recurrent and non-recurrent patients were compared,and the efficacy and influencing factors of thermal ablation for multiple T1N0M0 PTC were analyzed.Results:After over 5 years follow-up,a total of 11 patients(16.18％)relapsed,57 patients(83.82％)did not re-lapse.No lymph node and distant metastasis were found.No significant correlation was detected between the recurrence and clinical features,ultrasound findings and ablation parameters(P＞0.05).Among the patients with recurrence,1 patient underwent observation,2 patients underwent total thyroidectomy,and the other 8 patients successfully underwent secondary ablation,all of which had no obvious adverse reactions.Conclusion:The ablation of multiple PTC in T1N0M0 stage is safe and effective,with a recurrence rate of 16.18％over 5 years follow-up,and ablation has no effect on second treatment for recurrent patients.

Objective:To explore the effect of combining hand-brain perception training with hand-brain motor training based on mirror neuron theory on the recovery of upper limb function after a stroke.Methods:A group of 105 stroke survivors with upper limb dysfunction were randomly divided into a hand-brain perception (HP) group, a hand-brain motor (HM) group, and a combination (C) group, each of 35. In addition to conventional rehabilitation treatment (including exercise therapy, occupational therapy and physical factor therapy), the HP and HM groups were given hand-brain perception training and hand-brain motor training respectively, while group C was provided with both. Before the intervention and after 4 weeks, the upper limb motor functioning of all of the participants was assessed using the simplified version of the Fugl-Meyer upper limb motor function scale (FMA-UE). Sensory functioning was quantified using the tactile Semmes Weinstein monofilament examination (SWME), and the modified Barthel index (MBI) was used to quantify the participants′ ability in the activities of daily living.Results:After the intervention the average FMA-UE, MBI and SWME scores of all three groups had improved significantly, with group C′s average FMA-UE and MBI scores significantly better than the other two groups′ averages. The average SWME score of group C was then significantly better than that of group HM.Conclusions:Hand-brain perception combined with hand-brain motor training based on mirror neuron theory can further promote the recovery of upper limb sensory and motor functioning of stroke survivors., Such therapy is worthy of clinical promotion and application.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.