The ethical review of clinical research on rare diseases is crucial in ensuring the scientific validity of the research and the rights and interests of the subjects. Starting from the definition of rare diseases， this paper analyzed the current situation of domestic and international regulations and ethical review in clinical research on rare diseases. It also explored the key elements of ethical review from the two dimensions of scientific and ethical aspects of clinical research， including research objectives， methods， risk and benefit assessment， researcher qualifications， research infrastructure， informed consent process， data security and privacy protection， and protection of vulnerable groups such as children. Regarding the ethical review of clinical research on rare diseases， strategies can be adopted such as strengthening the training of ethics review personnel， conducting multi-center collaborative reviews， and focusing on the long-term safety of trials， to improve the quality of ethical review， protect the safety of the subjects， and ensure the efficiency and quality of clinical research.

AIM: To quantitatively evaluate the clinical characteristics of haze after transepithelial photorefractive keratectomy(TPRK)for astigmatism using corneal densitometry.METHODS:In this retrospective clinical study, a total of 74 patients(106 eyes)with astigmatism ≥1.25 D who underwent TPRK in our hospital from October 2022 to December 2024 were continuously collected. All of the study subjects were divided into transparent group(65 eyes)and haze group(41 eyes)based on whether haze occurred after surgery. Pentacam examination was performed before and after surgery, and corneal densitometry was recorded at the time points of preoperation, 1 mo postoperation in the transparent group and the most severe haze degree in the haze group. The collected corneal densitometry included the average densitometry of the entire corneal layer in the central 2 mm, 2-6 mm, and 6-10 mm areas, as well as the average densitometry of the entire layer of the corneal section in the center 6 mm of the astigmatism axis(astigmatism expressed in negative cylindrical form)and orthogonal axis(the axis perpendicular to the astigmatism axis), and the average densitometry of the entire layer of the corneal section in the nasal and temporal 2-6 mm areas of the astigmatism axis in the haze group of patients with regular astigmatism. The change in corneal densitometry after surgery compared with that before surgery was calculated.RESULTS:There was no statistically significant difference in baseline data such as gender, age, and spherical equivalent between the transparent group and the haze group(all P>0.05). The change in corneal densitometry in the 2-6 mm area of the haze group was greater than that in the transparent group(Z=-2.226, P=0.026), while there was no significant difference in the change of corneal densitometry in the central 2 mm and 6-10 mm areas between the two groups(both P>0.05). There was no significant difference in the change of corneal densitometry between the transparent group and haze group along the orthogonal axis(all P>0.05), while the change of corneal densitometry in the haze group along the astigmatism axis was greater than that in the transparent group(Z=-2.371, P=0.018). The temporal corneal densitometry of patients with regular astigmatism in the haze group after surgery was higher than that of the nasal side, and the change in corneal densitometry was also greater than that of the nasal side(Z=-4.288, P<0.001; Z=-4.043, P<0.001).CONCLUSION:Unlike spherical correction for myopia and hyperopia, haze after TPRK for astigmatism was mainly manifested in the peripheral cutting area of the astigmatism axis, and patients with regular astigmatism had a higher probability or severity of haze on the temporal side of the astigmatism axis than on the nasal side.

OBJECTIVES: To develop a risk prediction model for left ventricular adverse remodeling (LVAR) based on cardiac magnetic resonance (CMR) parameters in patients undergoing percutaneous coronary intervention (PCI) for acute ST-segment elevation myocardial infarction (STEMI). METHODS: A total of 329 acute STEMI patients undergoing primary PCI at 8 medical centers from January, 2018 to December, 2021 were prospectively enrolled. The parameters of CMR, performed at 7±2 days and 6 months post-PCI, were analyzed using CVI42 software. LVAR was defined as an increase >20% in left ventricular end-diastolic volume or >15% in left ventricular end-systolic volume at 6 months compared to baseline. The patients were randomized into training (n=230) and validation (n=99) sets in a 7∶3 ratio. In the training set, potential predictors were selected using LASSO regression, followed by univariate and multivariate logistic regression to construct a nomogram. Model performance was evaluated using receiver-operating characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis. RESULTS: LVAR occurred in 100 patients (30.40%), who had a higher incidence of major adverse cardiovascular events than those without LVAR (58.00% vs 16.16%, P<0.001). Left ventricular global longitudinal strain (LVGLS; OR=0.76, 95% CI: 0.61-0.95, P=0.015) and left atrial active strain (LAAS; OR=0.78, 95% CI: 0.67-0.92, P=0.003) were protective factors for LVAR, while infarct size (IS; OR=1.05, 95% CI: 1.01-1.10, P=0.017) and microvascular obstruction (MVO; OR=1.26, 95% CI: 1.01-1.59, P=0.048) were risk factors for LVAR. The nomogram had an AUC of 0.90 (95% CI: 0.86-0.94) in the training set and an AUC of 0.88 (95% CI: 0.81-0.94) in the validation set. CONCLUSIONS LVGLS, LAAS, IS, and MVO are independent predictors of LVAR in STEMI patients following PCI. The constructed nomogram has a strong predictive ability to provide assistance for management and early intervention of LVAR.
Humans ; Percutaneous Coronary Intervention ; Prospective Studies ; ST Elevation Myocardial Infarction/diagnostic imaging* ; Ventricular Remodeling ; Magnetic Resonance Imaging ; Male ; Female ; Middle Aged ; Risk Factors ; Aged ; Risk Assessment

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Background/Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is a significant risk factor for gallstone formation, but mechanisms underlying MASH-related gallstone formation remain unclear. Golgi membrane protein 1 (GOLM1) participates in hepatic cholesterol metabolism and is upregulated in MASH. Here, we aimed to explore the role of GOLM1 in MASH-related gallstone formation. Methods: The UK Biobank cohort was used for etiological analysis. GOLM1 knockout (GOLM1-/-) and wild-type (WT) mice were fed with a high-fat diet (HFD). Livers were excised for histology and immunohistochemistry analysis. Gallbladders were collected to calculate incidence of cholesterol gallstones (CGSs). Biles were collected for biliary lipid analysis. HepG2 cells were used to explore underlying mechanisms. Human liver samples were used for clinical validation. Results: MASH patients had a greater risk of cholelithiasis. All HFD-fed mice developed MASH, and the incidence of gallstones was 16.7% and 75.0% in GOLM1-/- and WT mice, respectively. GOLM1-/- decreased biliary cholesterol concentration and output. In vivo and in vitro assays confirmed that GOLM1 facilitated cholesterol efflux through upregulating ATP binding cassette transporter subfamily G member 5 (ABCG5). Mechanistically, GOLM1 translocated into nucleus to promote osteopontin (OPN) transcription, thus stimulating ABCG5-mediated cholesterol efflux. Moreover, GOLM1 was upregulated by interleukin-1β (IL-1β) in a dose-dependent manner. Finally, we confirmed that IL-1β, GOLM1, OPN, and ABCG5 were enhanced in livers of MASH patients with CGSs. Conclusions In MASH livers, upregulation of GOLM1 by IL-1β increases ABCG5-mediated cholesterol efflux in an OPN-dependent manner, promoting CGS formation. GOLM1 has the potential to be a molecular hub interconnecting MASH and CGSs.

Objective: To develop a modified calculation formula for renal tumor volume and tumor contact surface area (CSA) based on the modeling results of 3D Slicer software, and to create a webpage of the calculation formula for use. Methods: The general information and tumor anatomical data of 98 patients who underwent partial nephrectomy during Jan.2021 and Jul.2023 in the Second Affiliated Hospital of Xi'an Jiaotong University were retrospectively analyzed.The imaging data were input into 3D Slicer software in the form of Dicom files for tumor and ipsilateral kidney modeling to obtain tumor anatomical data.The relationship between tumor anatomical parameters and tumor volume and CSA was analyzed using multifactorial linear regression.The initial modified formulas (V2, C2) and the optimized modified formulas (V3, C3) for tumor volume over CSA were established, respectively, after insignificant variables were eliminated.The mean square error (MSE) and Akaike information criterion (AIC) of the modified and traditional formulas (V1, C1) were compared, and the formula with the smallest MSE and AIC was selected as the optimal tumor volume and CSA calculation formula.The median tumor volume and CSA obtained from 3D modeling were used as the cutoff values.The optimal formula and conventional formula were applied to calculate tumor volume and CSA for all patients, and risk stratification was performed for all patients based on these cutoff values, and the perioperative indicators of patients in the upper and lower groups were compared.Finally, an online calculation tool was developed based on HTML. Results: Based on multifactorial linear regression analysis, we obtained the modified tumor volume calculation formula: V=0.382abc+2.488a+2.372b－4.146c+1.948（V2）, V=0.469abc－4.586c+13.816（V3）; the modified tumor CSA calculation formula CSA=2.469a －2.262L －19.23a+6.206b+1.212c+18.017L+1.616h－3.97h －2.185h/h －0.388（C2）, CSA=2.376a －2.144L －20.157a+5.024b+1.128c+17.578L+2.525h－2.634（C3）.Both of the modified volume formula (MSE=151.298 vs. 127.807 vs. 104.106) and modified CSA formula (MSE=309.878 vs.23.556 vs.30.388) had smaller errors compared to the conventional formula.The modified volume calculation formula showed that bleeding was more and thermal ischemia time was longer in patients with larger tumor volumes than in patients with smaller tumor volumes (P<0.05); and the modified CSA calculation formula showed that bleeding was more, surgery and thermal ischemia time were longer in patients with high CSA than in patients with low CSA (P＜0.05).Finally, V3 and C3 are selected as the best calculation formula, and a web page (https://lizihao-bot.github.io/RCC-Calculate/) was established for easy use. Conclusion: This study combined data from a medical information technology platform with numerical modeling methods to provide a faster and more accurate method to calculate the renal tumor volume and CSA.Meanwhile, a webpage version of the tool was developed to enhance its practicability.

Background Air pollution and meteorological factors exert complex nonlinear effects on acute symptoms in the population, with intricate interactions among these factors. Traditional statistical methods struggle to simultaneously address complex nonlinear relationships and multicollinearity issues. Objective To delineate the dynamic effects of air pollutants and meteorological parameters on acute symptoms in three distinct populations with the multicollinearity being addressed and to generate reliable scientific evidence for prevention and control of health risk factors. Methods A time-series study design was employed to collect data on air pollution (daily mean temperature, daily precipitation, daily mean relative humidity, and daily mean wind speed), meteorological factors [Air Quality Index (AQI), fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and 8-hour maximum ozone (O₃)], and acute symptoms such as fever, cough, and sore throat in Jinan from June to December 2023. Key variables were selected using least absolute shrinkage and selection operator (LASSO) regression, followed by generalized additive mixed modeling (GAMM) to analyze the health effects of combined environmental exposures to air pollution and meteorological factors. Linear variables were modeled using linear mixed-effects function, nonlinear variables were smoothed using thin-plate regression splines, and variables with interaction effects were smoothed using low-rank scale-invariant tensor product splines. Fluctuations in independent variables following a normal distribution were treated as sampling errors and incorporated as random effects in the GAMM. Results For fever, the daily mean temperature, daily mean relative humidity, daily mean wind speed, and ambient SO₂ were statistically significant (P<0.05), with daily mean wind speed being a linear influencing factor. When the daily mean temperature was below 3 °C, each 10 °C increase corresponded to a relative risk (RR) of 2.64 (95%CI: 2.50, 2.79). When the daily mean temperature was ≥3 °C, each 10 °C increase corresponded to an RR of 0.86 (95%CI: 0.83, 0.89). Each 10% increase in daily mean relative humidity was associated with an RR of 0.93 (95%CI: 0.89, 0.97). Each 1 m·s⁻¹ increase in daily mean wind speed corresponded to an RR of 1.06 (95%CI: 1.02, 1.10). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.01 (95%CI: 0.98, 1.05), 1.21 (95%CI: 1.17, 1.24), and 0.97 (95%CI: 0.94, 0.99), respectively. For cough, the daily mean temperature, daily mean relative humidity, PM₁₀, and SO₂ were statistically significant (P<0.001), with PM₁₀ being a linear influencing factor. When the daily mean temperature was below 1 °C, each 10 °C increase corresponded to an RR of 1.47 (95%CI: 1.42, 1.52). When the daily mean temperature was ≥1 °C, each 10 °C increase corresponded to an RR of 0.85 (95%CI: 0.82, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.95 (95%CI: 0.92, 0.98). Each 50 μg·m⁻³ increase in PM₁₀ concentration corresponded to an RR of 1.05 (95%CI: 1.02, 1.08). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥ 12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.00 (95%CI: 0.97, 1.03), 1.12 (95%CI: 1.09, 1.16), and 0.98 (95%CI: 0.95, 1.00), respectively. For sore throat, the daily mean temperature, daily mean relative humidity, daily mean wind speed, PM₁₀, and SO₂ were statistically significant (P<0.05), with daily mean wind speed and PM₁₀ being linear influencing factors. When the daily mean temperature was below 2 °C, each 10 °C increase corresponded to an RR of 1.82 (95%CI: 1.69, 1.96). When the daily mean temperature was ≥2 °C, each 10 °C increase corresponded to an RR of 0.81 (95%CI: 0.77, 0.87). Each 10% increase in daily mean relative humidity was associated with an RR of 0.94 (95%CI: 0.88, 1.00). Within the concentration ranges of <10 μg·m⁻³, 10–<12.5 μg·m⁻³, and ≥12.5 μg·m⁻³, each 1 μg·m⁻³ increase in ambient SO₂ corresponded to RR values of 1.02 (95%CI: 0.97, 1.08), 1.13 (95%CI: 1.08, 1.19), and 0.98 (95%CI: 0.94, 1.02), respectively. Each 1 m·s⁻¹ increase in daily mean wind speed and each 50 μg·m⁻³ increase in PM₁₀ concentration were associated with RR values of 1.06 (95%CI: 1.00, 1.12) and 1.04 (95%CI: 0.98, 1.10), respectively. An interaction effect was observed between daily mean wind speed and PM₁₀: increasing daily mean wind speed non-linearly reduced the impact of PM₁₀, on sore throat whereas PM₁₀ had no significant effect on wind speed. Conclusion This study, by combining LASSO and GAMM, largely eliminates the multicollinearity among selected variables. It reveals complex non-linear effects and interactions between air pollutants, meteorological factors, and acute symptoms in different population groups in Jinan. The symptoms like fever, cough, and sore throat are non-linearly associated with daily mean temperature and SO₂ concentration, while PM₁₀ and wind speed show a linear relationship or interactive effects. These findings provide a new basis for the precise prevention and control of health risk factors.

Qingfeitang， specialized in resolving phlegm to stop cough and producing fluid to moisten dryness， is a classic prescription inherited and developed by physicians of successive generations and has been included in the Catalogue of Ancient Classic Prescriptions （First Batch） published by the National Administration of Traditional Chinese Medicine （TCM） in 2018. Relevant ancient books data and modern literature were collected by bibliometrics to analyze the historic origin， formula composition， herb origin， preparation methods， processing methods， clinical effect， and indications of Qingfeitang. The key information of Qingfeitang was summarized to provide reference for the clinical application of the decoction. In this study， a total of 43 pieces of effective data on relevant ancient literature， including 35 ancient TCM books， were collected based on a systematic collation of relevant historic and modern literature. Results showed that "Qingfeitang" was originated from the "Renshen Qingfeitang" recorded in the Taiping Holy Prescriptions for Universal Relief from the Qing dynasty. The name of "Qinfeitang" was first recorded in the Yeshi Luyanfang written by YE Dalian in the Song dynasty. We suggested the modern dosage and usage of Qingfeitang as follows： "Scutellariae Radix of 5.60 g， Platycodon grandiflora， Poria， Tangerine， Fritillaria， and Cortex Mori of 3.73 g respectively， Angelicae Sinensis Radix， Asparagi Radix， Gardeniae Fructus， Armeniacae Semen Amarum， and Ophiopogonis Radix of 2.61 g respectively， Schisandra of 1 g， and Glycyrrhizae Radix et Rhizoma of 1.12 g， and they were taken 3 times daily. The above formula is recommended to be decocted with 400 mL of water， with 3.37 g ginger and 6 g jujubae fructus， to 320 mL， and taken after a meal， three times per day". Qingfeitang has the effect of resolving phlegm to stop cough and producing fluid to moisten dryness， specialized in treating cough， asthma， rash， and other symptoms in ancient times. Modern applications are mainly focused on the respiratory system， used for treating diseases such as bronchopneumonia and cough. The above research results provide a reference basis for the later development and research of Qingfeitang.

OBJECTIVE: To investigate the clinical utility of novel of new hematological markers in the preoperative diagnosis of periprosthetic joint infection (PJI). METHODS: A retrospective analysis was conducted on a total of 149 patients who underwent revision of total hip arthroplasty (THA) or total knee arthroplasty (TKA) at a single center between January 2016 and June 2022, including 63 males and 86 females, aged from 47 to 93 years old with an average of (69.5±11.8) years old. Of them, 46 were diagnosed as PJI(PJI group), including 22 males and 24 females. The mean age was (71.3±12.5) years old. The body mass index (BMI) was (26.4±3.1) kg·m^-2. And 103 patients were diagnosed as aseptic prosthesis loosening (aseptic group), including 41 males and 62 females. The mean age was (68.7±11.4) years old. The BMI was (25.8±3.5) kg·m^-2. Preoperatively analyzed clinical parameters included C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), albumin, globulin, albumin-to-globulin ratio (AGR), plasma D-dimer, and plasma fibrinogen. The receiver operating characteristic curve (ROC), sensitivity, and specificity analysis were employed to compare the diagnostic value of each blood marker in preoperative PJI diagnosis. RESULTS: In the PJI group, the levels of CRP were 16.6 (7.6, 4.5) mg·L^-1, ESR was 17.0 (12.8, 35.5) mm·h^-1, plasma D-dimer was 1.0 (0.5, 3.1) μg·L^-1, and plasma fibrinogen was 4.2 (3.2, 3.1) mg·L^-1;all of which were higher compared to the aseptic group with CRP at 4.2 (2.6, 7.8) mg·L^-1, ESR at 12.0(8.0, 20.0 )mm·h^-l, D-dimer at 0.4(0.2, 0.7)μg·L^-1, and fibrinogen at 2.8(2.4, 3.3 ) g·L^-1(P<0.05). However, the albumin level of 35.3 (32.3, 37.5) g·L^-1 and the WBC ratio of 1.0(0.9, 1.1) in the PJI group were significantly lower compared to the aseptic group with levels of 39.8 (36.1, 41.8) g·L^-1 and 1.4 (1.3, 1.5), respectively (P<0.05). Only the area under the curve (AUC) of AGR and plasma fibrinogen were greater than 0.8. The optimal predictive cut-1off, AUC, sensitivity and specificity were 3.4 g·L-1, 0.820, 69.57% and 84.47% for plasma fibrinogen; 1.18, 0.813, 82.61% and 78.64% for AGR, respectively. CONCLUSION AGR and plasma fibrinogen are promising blood markers for improving the diagnosis of PJI.
Humans ; Female ; Fibrinogen/metabolism* ; Male ; Middle Aged ; Aged ; Prosthesis-Related Infections/blood* ; Retrospective Studies ; Biomarkers/blood* ; Aged, 80 and over ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthroplasty, Replacement, Hip/adverse effects* ; Leukocyte Count