BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

BACKGROUND: The choice of unicompartmental knee arthroplasty (UKA) vs . total knee arthroplasty (TKA) in the surgical treatment of knee osteoarthritis (KOA) remains controversial. This study aimed to perform a systematic review and meta-analysis of randomized controlled trials (RCTs) to compare the clinical results of UKA and TKA for treating unicompartmental KOA. METHODS: PubMed, Embase, and the Cochrane Library were systematically searched for articles published up to January 2, 2023. The literature was rigorously screened to include only RCTs comparing UKA and TKA for unicompartmental KOA. A systematic review and meta-analysis were performed to calculate the mean difference (MD), relative risk (RR), and 95% confidence interval (CI) according to the Cochrane standards. RESULTS: Thirteen publications involving 683 UKAs and 683 TKAs were analyzed. Except for one study with a follow-up period of 15 years, all outcome measures reported were within 5 years of follow-up. Meta-analysis showed better knee recovery (MD: 1.23; 95% CI: 1.01-1.45; P  <0.001), greater knee function (MD: 1.78; 95% CI: 0.34-3.22; P  = 0.020), less pain (MD: 0.75; 95% CI: 0.43-1.06; P  <0.001), and better health status (MD: 3.75; 95% CI: 0.81-6.69; P  = 0.010) after UKA than TKA. However, considering the minimal clinically important difference values for these variables, the findings were not clinically relevant. Moreover, UKA patients had fewer complications (RR: 0.59; 95% CI: 0.45-0.78; P  <0.001) and shorter hospital stays (MD: -0.89; 95% CI: -1.57 to -0.22; P  = 0.009) than did TKA patients. There were no statistically significant differences in terms of postoperative range of movement, revision, failure, operation time, and patient satisfaction. CONCLUSIONS In terms of clinical efficacy, there was no obvious advantage of UKA over TKA in the surgical treatment of knee OA when considering the minimal clinically important difference. The main advantage of UKA over TKA is that it leads to fewer complications and a shorter length of hospital stay. It is ideal to perform prospective studies with longer follow-up periods to fully evaluate the long-term efficacy and safety of the two procedures in the future.
Humans ; Arthroplasty, Replacement, Knee/methods* ; Osteoarthritis, Knee/surgery* ; Randomized Controlled Trials as Topic ; Treatment Outcome

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

A pyrene-phenol-based fluorescent probe PyP which showed typical intramolecular charge transfer(ICT)and monomer-excimer activities was synthesized by using pyrene carboxaldehyde hydrazone and 4-tert-butyl-2,6-diformylphenol as the raw materials.The effects of solvents on PyP were studied,and the results showed that the color of protic polar solvents(Ethanol,N,N-dimethylformamide,methanol and H2O)were successfully identified.Based on the solvent polarity-regulated PyP monomer-excimer switching,the rapid and highly sensitive ratiometric probe,"Turn-off"and"Turn-on"multimodal probes were established for detection of trace water content in organic solvents(Dimethyl sulfoxide,N,N-dimethylformamide,ethanol and methanol),with detection limits(3σ/k)of 0.0021％,0.046％,0.062％and 0.024％.The method was successfully used to detect water content in dimethyl sulfoxide,N,N-dimethy lformamide,ethanol and methanol commercial organic solvents,with recoveries ranging from 97.2％to 108.0％.The developed method showed good accuracy and stability,and had good application prospect.

Using a novel(OH)n-C60@SiO2@Tm2O3@Ca5(PO4)3(OH)quaternary nano-particles/cross-linked chitosan coated open-tubular capillary column(QNPsC-OTCC)as the analytical column,a new method for highly selective determination of taurine(TAU)in functional drinks using pre-column derivatization capillary electrochromatography coupled with electrochemiluminescence(CEC-ECL)detection was established.In the experiments,it was found that adding hexamethylenetetramine as a co-catalyst in N-methylation derivative reaction could quantitatively convert TAU into a single derivative product that cuold be detected by ECL.With the help of Ru(bpy)32+reagent,the ECL peak intensity of TAU derivative was increased by more than 1000 times compared to the original TAU.In addition,a Ru-containing d-f cyano-bridged heterometallic coordination polymer modified platinum electrode was used instead of a bare platinum electrode as working electrode for ECL detection,which resulted in a further increase of the peak response of TAU derivatives about 5.7 times.Under optimized analytical conditions,by using betastatin hydrochloride(BSH)as the internal standard and simultaneously derivatized with TAU,the relative ratio of peak intensity of TAU and BSH derivatives showed a linear relationship with the initial TAU concentration in a two-segment ranges of 0.2-6.0 mmol/L and 6.0-10 mmol/L.The limit of detection of TAU was 0.09 mmol/L(S/N=3).The developed method was applied to determination of TAU contents in four commercial functional drink samples,and the relative standard deviations(RSDs)for relative intensity ratio were less than 0.9％,and the recoveries were in the range of 95.0％～102.0％,indicating good practicability of the method.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Background Improper settings of outdoor smoking points in public places may increase the risk of secondhand smoke exposure among the population. Conducting research on air pollution in and around smoking spots and related influencing factors can provide valuable insights for optimizing the setting of outdoor smoking points. Objective To investigate the influence of the number of smokers at outdoor smoking points and the distance on the diffusion characteristics of surrounding air pollutants, in order to optimize the setting of outdoor smoking points. Methods Surrounding the exhibition halls in the China International Import Expo (CIIE), two outdoor smoking points were randomly selected, one on the first floor (ground level) and the other on the second floor (16 m above ground), respectively. At 0, 3, 6, and 9 m from the smoking points in the same direction, validated portable air pollutant monitors were used to measure the real-time fine particulate matter (PM_2.5) and carbon dioxide (CO₂) concentrations for consecutive 5 d during the exhibition, as well as the environmental meteorological factors at 0 m with weather meters including wind speed, wind direction, and air pressure. An open outdoor atmospheric background sampling point was selected on each of the two floors to carry out parallel sampling. Simultaneously, the number of smokers at each smoking point were double recorded per minute. The relationships between the number of smokers, distance from the smoking points, and ambient PM_2.5 and CO₂ concentrations were evaluated by generalized additive regression models for time-series data after adjustment of confounders such as temperature, relative humidity, and wind speed. Results The median numbers of smokers at smoking points on the first and second floors were 6 [interquartile range (IQR): 3, 9] and 9 (IQR: 6, 13), respectively. Windless (wind speed <0.6 m·s⁻¹) occupied most of the time (85.9%) at both locations. The average concentration of ambient PM_2.5 at the smoking points (0 m) [mean ± standard deviation, (106±114) μg·m⁻³] was 4.2 times higher than that of the atmospheric background [(25±7) μg·m⁻³], the PM_2.5 concentration showed a gradient decline with the increase of distance from the smoking points, and the average PM_2.5 concentration at 9 m points [(35±22) μg·m⁻³] was close to the background level (1.4 times higher). The maximum concentration of CO₂ [(628±23) μmol·mol⁻¹] was observed at 0 m, and its average value was 1.3 times higher than that of the atmospheric background [(481±40) μmol·mol⁻¹], and there was no gradient decrease in CO₂ concentration with increasing distance at 0, 3, 6, and 9 m points. The regression analyses showed that, taking smoking point as the reference, every 3 m increase in distance was associated with a decrease of ambient PM_2.5 by 24.6 [95% confidence interval (95%CI): 23.5, 25.8] μg·m⁻³ (23.2%) and CO₂ by 54.1 (95%CI: 53.1, 55.1) μmol·mol⁻¹ (8.6%). Every one extra smoker at the smoking point was associated with an average increase of PM_2.5 and CO₂ by 2.0 (95%CI: 1.7, 2.8) μg·m⁻³ and 1.0 (95%CI: 0.7，1.2) μmol·mol⁻¹, respectively. The sensitivity analysis indicated that, under windless conditions, the concentrations of PM_2.5 and CO₂ at the smoking points were even higher but the decreasing and dispersion characteristics remained consistent. Conclusion Outdoor smoking points could significantly increase the PM_2.5 concentrations in the surrounding air and the risks of secondhand smoke exposure, despite of the noticeable decreasing trend with increasing distance. Considering the inevitable poor dispersion conditions such as windless and light wind, outdoor smoking points are recommended to be set at least 9 m or farther away from non-smoking areas.

Depression is a prevalent mental illness worldwide, its multifaceted pathogenesis is still in the exploratory stage. MicroRNA (miRNA), as a crucial epigenetic regulator, plays an important role in depression. miR-124 is one of the most abundant miRNAs in the central nervous system including neurons and microglia, and involved in various biological events like neuron development and differentiation, synaptic and axonal growth, neural plasticity, inflammation and autophagy. Recent studies have reported abnormal expression of miR-124 in both depression patients and animal models. Most of the studies showed that miR-124 is upregulated in the hippocampus or prefrontal cortex in stress-induced rodent depression animal models such as CUMS, CSDS, CORT, CRS and LH but some evidence for divergence. Upregulation of miR-124 expression may be involved in depression-like behavior via CREB/BDNF/TrkB pathway, GR pathway, SIRT1 pathway, apoptosis and autophagy pathways by directly targeting these genes including Creb, Bdnf, Sirt1, Nr3c1, Ezh2 and Stat3. The downregulation of miR-124 expression in neurons is mainly involved in the neurogenesis and neuroplasticity impairments in depression by targeting the Notch signaling pathway and DDIT4/TSC1/2/mTORC1 pathway. The downregulation of miR-124 expression also was found in the activated microglia in the stress-induced models, and resulted in neuroinflammation. In summary, the abnormal expression of miR-124 in the brain of depression-related models and its related mechanisms are complex and even contradictory, and still need further research. This review provides a summary of the research progress of miR-124 in depression.

Cells not only contain membrane-bound organelles (MBOs), but also membraneless organelles (MLOs) formed by condensation of many biomacromolecules. Examples include RNA-protein granules such as nucleoli and PML nuclear bodies (PML-NBs) in the nucleus, as well as stress granules and P-bodies in the cytoplasm. Phase separation is the basic organizing principle of the form of the condensates or membraneless organelles (MLOs) of biomacromolecules including proteins and nucleic acids. In particular, liquid-liquid phase separation (LLPS) compartmentalises and concentrates biological macromolecules into liquid condensates. It has been found that phase separation of biomacromolecules requires some typical intrinsic characteristics, such as intrinsically disordered regions, modular domains and multivalent interactions. The phase separation of biomacromolecules plays a key role in many important cell activities. In recent years, the phase separation of biomacromolecules phase has become a focus of research in gene transcriptional regulation. Transcriptional regulatory elements such as RNA polymerases, transcription factors (TFs), and super enhancers (SEs) all play important roles through phase separation. Our group has previously reported for the first time that long-term inactivation or absence of assembly factors leads to the formation of condensates of RNA polymerase II (RNAPII) subunits in the cytoplasm, and this process is reversible, suggesting a novel regulatory model of eukaryotic transcription machinery. The phase separation of biomacromolecules provides a biophysical understanding for the rapid transmission of transcriptional signals by a large number of TFs. Moreover, phase separation during transcriptional regulation is closely related to the occurrence of cancer. For example, the activation of oncogenes is usually associated with the formation of phase separation condensates at the SEs. In this review, the intrinsic characteristics of the formation of biomacromolecules phase separation and the important role of phase separation in transcriptional regulation are reviewed, which will provide reference for understanding basic cell activities and gene regulation in cancer.