Objective: To evaluate the efficacy and safety of ruxolitinib combined with low-dose hormone for patients with acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: Thirty patients with aGVHD after allo-HSCT admitted to the Department of Hematology of the General Hospital of Western Theater Command from November 2021 to November 2024 were retrospectively analyzed. All patients were treated with low-dose hormone (methylprednisolone 0.3-1 mg kg -d ) combined with ruxolitinib 5-10 mg d . The efficacy and adverse reactions were observed during the follow-up period to analyze the survival outcomes of the patients. Results: A total of 30 patients with aGVHD after allo-HSCT were included in this study, consisting of 15 (50%) males and 15 (50%) females with a median age of 34 year-old (ranging from 14 to 62). Classification by disease type: there were 18 cases of acute myeloid leukemia, 4 cases of acute lymphoblastic leukemia, 4 cases of aplastic anemia, and 4 cases of myelodysplastic syndrome. Classification by aGVHD severity: there were 27 cases (90%) of Ⅱ-Ⅳ degree aGVHD and 11 cases (36.7%) of Ⅲ-Ⅳ degree aGVHD. Ruxolitinib in combination with low-dose glucocorticoid treatment yield responses in 28 (93.3%) patients, of which 27 (90%) achieved complete remission (CR), while 1 (3.3%) showed partial remission (PR). One patient (3.3%) had no response (NR), and 1 patient (3.3%) exhibited progressed disease (PD). Overall survival (OS) at 1 year of transplantation was 73.9% (95%CI 49.5% to 87.7%), progression-free survival (PFS) was 93.3% (95%CI 75.9% to 98.3%), non-relapse mortality (NRM) was 20.6% (95%CI 7.9% to 47.4%), and median survival time was 27.6 months. Conclusion: Ruxolitinib combined with low-dose hormones is safe and effective in the treatment of aGVHD after allo-HSCT.

Background: Postoperative hypokalemia is a common electrolyte disturbance associated with adverse outcomes, particularly in older adults. This study aimed to identify risk factors and develop predictive models for hypokalemia within 24 hours after carotid endarterectomy (CEA) for severe carotid artery stenosis, a condition that primarily affects older patient populations. Methods: A retrospective cohort of 1,076 CEA patients (October 2021 to May 2023) was analyzed. Risk factors were identified using univariate and multivariate logistic regression. A predictive nomogram was developed and internally validated via bootstrapping. Machine learning models (Random Forest and XGBoost) were developed and interpreted using SHAP (SHapley Additive exPlanations) analysis. Subgroup analyses were performed in patients aged ≥70 years and by comparing postoperative potassium levels >4.0 mmol/L versus 3.5–4.0 mmol/L. Results: The cohort had a median age of 65 years. Multivariate analysis identified preoperative potassium (odds ratio [OR]=0.60, 95% confidence interval [CI] 0.50–0.72), hemoglobin (OR=0.74, 95% CI 0.63–0.88), BMI (OR=0.74, 95% CI 0.63–0.88), and postoperative visual analogue scale score (OR=1.28, 95% CI 1.09–1.51) as independent predictors. Frailty showed borderline significance (OR=1.56, 95% CI 1.00–2.44, p=0.05). The nomogram achieved an area under the curve (AUC) of 0.710, demonstrating good discrimination and calibration. Machine learning models similarly performed well (AUC 0.707–0.709). Conclusion We developed a validated tool to predict postoperative hypokalemia after CEA. The model highlights that in addition to biochemical and surgical factors, geriatric syndromes like frailty and nutritional status are pivotal risk determinants. This facilitates early, individualized management, including tailored potassium supplementation, nutritional support, and pain control, especially for vulnerable older adults, to mitigate complications and promote recovery.

Background: Early and accurate differentiation of benign and malignant breast nodules is crucial for determining appropriate therapeutic strategies. Recently, artificial intelligence (AI)-based diagnostic systems have been increasingly utilized to enhance the diagnostic performance of ultrasound imaging. Aim: To evaluate the diagnostic value of artificial intelligence-based ultrasound (AI-US) in differentiating benign and malignant breast nodules and to compare its performance with conventional ultrasound (US). Materials and Methods: This retrospective study included 101 patients with 103 breast nodules (58 malignant, 45 benign) confirmed by histopathological analysis (biopsy or surgery). Diagnostic performance was evaluated across three groups: 1) Conventional ultrasound (US), 2) AI system (AI group), and 3) Combined US and AI system (US+AI group). Sensitivity (SE), specificity (SP), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve (AUC) were calculated and compared. Results: Of the 103 nodules, 56.3% were malignant and 43.7% were benign. The AI group demonstrated higher diagnostic metrics than the US group—SE (82.76%), SP (80.00%), ACC (81.55%), and AUC (0.814)—though the difference was not statistically significant (p>0.05). However, the combined US+AI group showed significant improvement, reaching an SE of 87.93%, SP of 86.67%, ACC of 87.38%, and an AUC of 0.874. These results were statistically superior to the conventional US group (p<0.05). Conclusion The AI-based ultrasound diagnostic system significantly improves the accuracy of differentiating breast nodules. This technology serves as a reliable decision-support tool, particularly for reducing diagnostic errors among less experienced physicians and minimizing unnecessary invasive biopsies.

Objective::To investigate the correlation between maternal serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels and gestational duration in pregnant women with pulmonary hypertension (PH).Methods::The study included pregnant individuals with PH stemming from mitral valve stenosis and mitral valve regurgitation (post-capillary PH) or pulmonary arterial hypertension (pre-capillary PH) who were admitted to Guangdong Provincial People’s Hospital between January 1, 2014 and December 31, 2020. In this retrospective cohort study, maternal serum NT-proBNP levels during pregnancy, along with other clinical data, were obtained from structured electronic medical records. These data included gestational age at delivery, echocardiographic parameters, laboratory findings, gestational duration, delivery mode, and other relevant clinical variables. Univariate and multivariate regression analyses were conducted to assess the association between NT-proBNP levels and gestational duration. Adjustments were made for potential confounding factors, and curve fitting and threshold effect analysis were employed to identify tangent points. Furthermore, stratified analyses were performed based on tricuspid regurgitation velocity, maternal age, and parity.Results::A total of 64 patients with post-capillary PH and 74 patients with pre-capillary PH were included in this study. Among patients with post-capillary PH, the results of multivariate regression analysis indicated a significant association between maternal NT-proBNP levels and gestational duration (β = -0.03, 95% confidence interval (CI) -0.05 to 0.00, P = 0.02). The fitted curve demonstrated a negative correlation between maternal NT-proBNP levels and gestational duration, with a significant break point at 379.9 ng/L ( P < 0.05). In the post-capillary PH group, the stratified analysis revealed a regression coefficient of -0.05 (95% CI:-0.06 to -0.04, P = 0.001) in patients with a tricuspid regurgitation velocity >340 mm/s. For patients >35 years old, the regression coefficient was -0.03 (95% CI -0.06 to -0.01, P = 0.02). In multiparous women, the regression coefficient was -0.03 (95% CI-0.06 to 0.00, P = 0.03). Conclusion::In pregnant women with pulmonary hypertension, maternal NT-proBNP levels are associated with gestational duration, particularly with an increased risk of preterm labor.

Objective To investigate the diagnostic value of our regression model based on quantitative parameters of dual-energy CT and clinical features for stages T2 and T3 colorectal cancer.Methods A cross-section study was performed on 91 patients with colorectal cancer confirmed by postoperative pathology in our hospital from January 2022 to November 2023.All of them underwent dual-energy CT examination.According to the pathological T staging criteria of Chinese Colorectal Cancer Diagnosis and Treatment Standard(2020 Edition),they were divided into T2 group(n=43)and T3 group(n=48).Univariate analysis was used to compare the differences in quantitative CT parameters and clinical features between the 2 groups,and the obtained significant variables were employed to construct diagnosis models by univariate or multivariate logistic regression analysis.The area under receiver operating characteristic curve(AUC)of the CT parametric model and the model combined with clinical features was compared to evaluate the efficacy of diagnosing T2 and T3 stages.Results Univariate analysis showed that carcinoembryonic antigen(CEA),N stage,tumor location,tumor longest diameter(LD),CT value of virtual noncontrast(CT-VNC),fat fraction,electron density(Rho)and dual energy index(DEI)were significantly different between the T2 and T3 groups(P＜0.05).Multivariate logistic regression analysis found that N stage,tumor location,LD,fat fraction and DEI were independent risk factors for the diagnosis of stage T3.The AUC value of the model of above CT parameters in diagnosing stage T3 colorectal cancer was 0.671(95％CI:0.558～0.783),and the AUC value of the combined model of above CT parameters and clinical features was 0.886(95％CI:0.815～0.957),and statistical difference was observed in the AUC value between the combined model and the CT parametric model(P＜0.01).Conclusion The regression model constructed with dual-energy CT quantitative parameters combined with clinical features has high value in the preoperative diagnosis of stages T2 and T3 colorectal cancer before surgery.

Colorimetric analysis is a detection and quantification method based on observable color changes in response to analytes,which offers significant advantages including visually detectable signals,straightforward operation,rapid response,and low cost.Consequently,it plays a crucial role in a variety of fields.With increasingly diverse and complex application,colorimetric analysis requires continuous improvement in sensitivity,adaptability to diverse detection environments,and complex data handling capabilities.In recent years,the development of artificial intelligence technology,particularly within its core domain of machine learning(ML),has led to significant advancements in colorimetric analysis.The ML-assisted colorimetric analysis enables high-throughput and high-sensitivity detection,alongside automated analysis,thereby providing novel strategies to overcome the inherent limitations.This review categorized machine learning techniques and summarized their application in colorimetric analysis,introducing two fundamental categories of supervised learning,and unsupervised learning based on the division of core learning paradigms.The research progress of ML-assisted colorimetric analysis in the fields of environmental monitoring,biochemical detection,and food safety were summarized.Finally,the current challenges facing by this research area were analyzed and the research prospect of ML-assisted colorimetric analysis was outlined.

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Neuraxial opioids, widely used in obstetric and perioperative pain management, often lead to unwanted itch, reducing patient satisfaction. While the μ-opioid receptor has been implicated in opioid-induced itch, the genetic basis for variable itch incidence remains unknown. This study examined 3616 patients receiving epidural opioids, revealing an itch occurrence of 26.55%, with variations among opioid types and gender. Analysis of the OPRM1 gene identified six single-nucleotide polymorphisms, notably rs1799971 (A118G), that correlated with opioid-induced itch. Mouse models with an equivalent A112G mutation showed reduced neuraxial opioid-induced itch and light touch-evoked itch, mirroring human findings. The 118G allele demonstrated an anti-itch effect without impacting analgesia, addiction, or tolerance, offering insights for risk stratification and potential anti-itch pretreatment strategies.
Receptors, Opioid, mu/genetics* ; Pruritus/chemically induced* ; Humans ; Analgesics, Opioid/administration & dosage* ; Female ; Male ; Animals ; Polymorphism, Single Nucleotide/genetics* ; Adult ; Mice ; Middle Aged

Clear aligner treatment is a novel technique in current orthodontic practice. Distinct from traditional fixed orthodontic appliances, clear aligners have different material features and biomechanical characteristics and treatment efficiencies, presenting new clinical challenges. Therefore, a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique. This expert consensus discusses case selection and grading of treatment difficulty, principle of clear aligner therapy, clinical procedures and potential complications, which are crucial to the clinical success of clear aligner treatment.
Humans ; Consensus ; Orthodontic Appliance Design ; Orthodontic Appliances, Removable ; Tooth Movement Techniques/methods* ; Malocclusion/therapy* ; Orthodontics, Corrective/instrumentation*

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