Neurofibromatosis type 1 (NF1) presents with a diverse range of symptoms that can affect the skin, bones, eyes, central nervous system, and other organs. This article reports the diagnosis and treatment process of a patient with NF1 complicated by bilateral severe-to-profound sensorineural hearing loss. Genetic testing revealed a heterozygous variant of NF1 NM_ 000267.3: c.4054_ 4058del(p.Ser1352LeufsTer20, supporting the diagnosis of NF1. After thorough evaluation, a right cochlear implantation was performed, yielding satisfactory postoperative results. This case suggests that NF1 patients may exhibit phenotypic heterogeneity and atypicality, providing a reference for clinicians in the diagnosis and treatment of such patients.

This study identified six novel azaphilones, isochromophilones G-L (1-6), and three novel biosynthetically related congeners (7-9) from Diaporthe sp. SCSIO 41011. The structures and absolute configurations were elucidated through comprehensive spectroscopic analyses combined with experimental and calculated electronic circular dichroism (ECD) spectra. Significantly, three highly oxygenated azaphilones contain an acetyl group at the terminal chain (4) or linear conjugated polyenoid moieties (5 and 6), which occur infrequently in the azaphilone family. Additionally, several compounds demonstrated inhibition of lipopolysaccharide (LPS)-induced nuclear factor kappa-B (NF-κB) activation in RAW 264.7 macrophages at 20 μmol·L^-1. The novel compound (1) effectively inhibited receptor activator of NF-κB ligand (RANKL)-induced osteoclast differentiation without exhibiting cytotoxicity in bone marrow and RAW 264.7 macrophages, indicating its potential as a promising lead compound for osteolytic disease treatment. This research presents the first documented evidence of azaphilone derivatives as inhibitors of RANKL-induced osteoclastogenesis.
Animals ; Mice ; RANK Ligand/genetics* ; RAW 264.7 Cells ; Osteoclasts/metabolism* ; Benzopyrans/isolation & purification* ; Osteogenesis/drug effects* ; Macrophages/metabolism* ; Molecular Structure ; Pigments, Biological/isolation & purification* ; Ascomycota/chemistry* ; NF-kappa B/genetics* ; Cell Differentiation/drug effects*

Objective To systematic analyze the risk factors for new-onset urethral stricture after transurethral holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH).Methods A case-control study was conducted on 746 BPH patients undergoing HoLEP treatment in Department of Urology of the Third Medical Center of Chinese PLA General Hospital from November 2021 to August 2024.After 23 cases were excluded because of complication of prostate cancer,finally 723 patients were included.General clinical data such as age,height,weight,history of smoking and drinking,perioperative parameters,and follow-up data at 1,3 and 6 months after operation were collected.Univariate and multivariate logistic regression analyses were used to identify the clinical risk factors for new-onset urethral stricture after HoLEP.Results The subjected patients had a median age of 66.5(64.0,75.0)years,and a preoperative median prostate volume of 66(45,92)mL,and a median indwelling catheter time of 4(4,5)d.The incidence of new urethral stricture after operation was 5.8%(42/723),with membranous part of the urethra(61.9%)the most common site,followed by the external urethral orifice(21.4%)and the bladder neck(7.1%).Risk factor analysis indicated that low BMI(<18.5 kg/m2)(OR=4.682,P=0.037),young age(OR=0.946,P=0.005),and postoperative urinary tract infection(OR=4.513,P=0.001)were independent risk factors for new-onset urethral stricture after surgery.Prostate volume and indwelling time of urinary catheter had no significant association with the occurrence of new urethral stricture after surgery.Conclusion The occurrence of new-onset urethral stricture after HoLEP is significantly correlated with BMI,age and urinary tract infection.The above 3 factors can be used as better predictors of new-onset urethral stricture after HoLEP.

Objective:To investigate the current status of occupational stress among electronic manufacturing workers in Qingdao and analyze its effect on depressive symptoms and anxiety symptoms.Methods:From July to September 2022, a cluster random sampling method was employed to select frontline workers from 14 electronic manufacturing enterprises in Qingdao as the study subjects. A total of 1134 questionnaires were distributed, with 1000 valid questionnaires collected, yielding an effective response rate of 88.18%. The Core Occupational Stress Scale (COSS), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were adopted to assess occupational stress, depressive symptoms, and anxiety symptoms, respectively. Binary logistic regression was employed to analyze influencing factors of occupational stress and its effects on depressive and anxiety symptoms.Results:The detection rates of occupational stress, depressive symptoms, and anxiety symptoms were 15.1% (15/1000), 7.9% (79/1000), and 13.8% (138/1000), respectively. The influencing factor analysis of occupational stress revealed that female gender ( OR=0.621, 95% CI: 0.434-0.890) and weekly working hours of 45-48 hours ( OR=0.537, 95% CI: 0.309-0.935) were protective factors ( P<0.05), while weekly working hours ≥55 hours ( OR=2.176, 95% CI: 1.290-3.670) and shift work ( OR=2.038, 95% CI: 1.412-2.940) were risk factors ( P<0.05). Spearman correlation analysis showed that the COSS score was positively related with the PHQ-9 score and the GAD-7 score ( rs=0.438, 0.289, P<0.01). Workers judged with occupational stress had 3.596 times (95% CI: 2.181-5.931, P<0.001) and 3.121 times (95% CI: 2.046-4.761, P<0.001) higher risks of detecting depressive and anxiety symptoms compared to those without occupational stress. Conclusion:Electronic manufacturing workers in Qingdao experience a certain level of occupational stress, which may increase the risk of developing depressive and anxiety symptoms. Enterprises should implement appropriate interventions to promote workers' mental health.

Objective To evaluate the modeling characteristics and predictive performance of models for predicting post-stroke neurological deterioration(ND)published in existing literatures.Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidance for scoping reviews,a comprehensive search was conducted in PubMed,CINAHL,Cochrane Library,Embase,Web of Science,Scopus,CNKI,Wanfang Data,and VIP databases from inception to December 15,2024.The search strategy combined Medical Subject Headings(MeSH)and free-text terms,with key words including"Stroke""Ischemic Stroke""Neurological Deterioration""Nomograms""Risk Prediction""Predictive Models""卒中""脑梗死""脑出血""神经功能恶化"and"预测模型".Base on the data extraction checklist and critical appraisal,data extraction covered three domains:(1)basic characteristics,including author,publication year,country,study design(retrospective,prospective,registry-based),sample source(single-center,multicenter),stroke subtypes(acute ischemic stroke[AIS]-conservative therapy,AIS-intravenous thrombolysis[IVT],AIS-endovascular therapy[EVT],intracerebral hemorrhage[ICH]),ND time windows(acute[≤72 h],subacute[≤ 7 d],long-term[≤90 d]),and outcome types(single/composite endpoints);(2)model evaluation metrics,including missing data handling(complete-case analysis,multiple imputation),model development methodologies(multivariate Logistic regression,least absolute shrinkage and selection operator regression,machine learning),presentation formats(nomograms,web calculators,risk prediction tool),discrimination(area under the curve,C-index),calibration(Hosmer-Lemeshow test,calibration curve and slope),clinical utility(decision curve analysis[DCA],global metrics Brier score,R2,AIC),sample size(training set,internal validation set,external validation set),sample size requirements(events per variable[EPV]≥10 to mitigate overfitting),and validation(internal/external);(3)predictor features,including selection strategies(prior knowledge-driven,univariate analysis),quantity,and attributes(demographics,medical history,physical examination,treatment intervention information,imaging/laboratory indicators).Predictive models that meet exclusion criteria from prior literature were analyzed by their discrimination,calibration,clinical utility and global metrics.Forest plots were utilized to visualize discrimination(evaluated via difference in area under the curve)of the extracted models.The prediction model risk of bias assessment tool(PROBAST)was applied to assess bias risk and clinical applicability.Occurrence frequencies of the post-stroke neurological deterioration predictors were ranked and the top 6 high-frequency predictors were extracted.Results(1)Among 3 728 screened studies,25 were included based on the inclusion and exclusion criteria.(2)Basic characteristics:retrospective(72％[18/25])and single-center(64％[16/25])designs dominated.With most models targeted on AIS(92％[23/25]),and the rest(8％[2/25])on ICH.ND was primarily defined by neurological scale changes(60％[15/25];e.g.,National Institutes of Health stroke scale[NIHSS]score increase or Glasgow coma scale[GCS]score decrease),with time windows categorized as acute(36％[9/25]),subacute(48％[12/25]),or long-term(16％[4/25]).(3)Model evaluation:multivariate Logistic regression(96％[24/25])and nomograms(88％[22/25])were predominant.Only 24％(6/25)explicitly addressed missing data handling methods,and 52％(13/25)with EPV≥10.The median area under the curve was 0.865(range:0.650-0.981).44％(11/25)of the studies reported calibration curves,and 4％(1/25)reported calibration slopes.All studies utilized DCA to validate their clinical applicability,84％(21/25)of the studies conducted internal validation,while only 32％(8/25)conducted external validation.PROBAST evaluation revealed low overall bias risk in 8％(2/25;no error across participant,predictor,outcome,or analysis domains)and low clinical applicability risk in 44％(11/25;alignment with target populations,accessible predictors,and clinically relevant outcomes)of the studies.(4)Predictors:64％(16/25)of the predictor were screened predominantly through the prior knowledge-driven based strategy.The top 6 high-frequency predictors are NIHSS score(64％[16/25]),age(36％[9/25]),blood glucose/diabetes(36％[9/25]),blood pressure/hypertension(32％[8/25]),the Alberta stroke program early CT score(20％[5/25]),and neutrophil-to-lymphocyte ratio(20％[5/25]).AIS-ND predictors emphasized readily available metrics,such as NIHSS(65％[15/23]),age(35％[8/23]),while ICH-ND primarily relied on imaging markers(e.g.,baseline hematoma volume[2/2],location[1/2]).Conclusion Current post-stroke ND predictive models demonstrate satisfactory performance on discrimination and multimodal integration,but their practical application are hindered by insufficient calibration quantification,high bias risk,and limited clinical translatability.

Objective:To investigate the differences in protein profile expression in serum samples from children with corona virus disease 2019（COVID-19）related encephalopathy and to explore the underlying mechanisms.Methods:From December 1,2022 to January 31,2023,28 children with COVID-19 who were admitted to the Department of Pediatric Intensive Medicine at Shandong Provincial Hospital Affiliated to Shandong First Medical University were collected,including 21 patients with encephalopathy(COVID-19 with encephalopathy group) and seven patients without encephalopathy(COVID-19 without encephalopathy group).Three children from each group were selected for serum proteomic analysis using tandem mass spectrometry labeling proteomics technology.Proteins were considered significantly different if the fold change was >1.2 or <0.8，with P<0.05.Bioinformatics analysis，including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment were performed on differentially expressed proteins.Protein-protein interaction networks were analyzed using the STRING database.Selected proteins were further validated by enzyme-linked immunosorbert assay. Results:A total of 41 differentially expressed proteins were identified between the two groups.Among these，14 proteins were upregulated and 27 proteins were downregulated in COVID-19 patients with encephalopathy compared to those without encephalopathy.Bioinformatics analysis revealed that these proteins were primarily enriched in critical signaling pathways，including complement and coagulation regulation，neutrophil degranulation and activation，and platelet degranulation.Enzyme-linked immunosorbert assay validation confirmed significant differences in key coagulation-regulating proteins(von willebrand factor upregulated，serpin family F member 2 downregulated in COVID-19 patients with encephalopatly）between the two groups.Conclusion:Coagulation dysfunction may play a role in the development of COVID-19 associated encephalopathy in children，providing valuable insights for future research.

Sarcopenia is a systemic disease characterized by accelerated loss of skeletal muscle mass and function,leading to an increased incidence of adverse outcomes such as falls and fractures.Sarcopenia is classified into primary and secondary types,with primary sarcopenia being closely related to aging and posing a serious threat to a healthy life among the elderly.Sarcopenia has an insidious onset and is often overlooked in terms of its clinical treatment.Its pathogenesis is complex,involving functional changes and pathological alterations in multiple systems,and presenting major research challenges.Cell models can effectively be used to simulate the pathological changes of diseases under controllable conditions,thus facilitating the investigation of the etiology and factors influencing sarcopenia,and providing an important approach for in-depth studies of its mechanism;however,there is currently no standardized cell model in the field of sarcopenia research.Commonly used cell models currently include models involving protein metabolism interventions,oxidative stress,and inflammatory response interventions.This review considers the commonly used skeletal muscle cell types and modeling method of sarcopenia,to provide a solid foundation and important method ological reference for further simulation of the pathological process of sarcopenia in subsequent experimental studies.

Objective:To explore the intervention effect of health nutrition model based on digital management on patients with gestational diabetes mellitus, and to provide reference for blood sugar control and pregnancy outcome improvement of gestational diabetes mellitus.Methods:Using a randomized controlled study method, totally 80 cases of gestational diabetes mellitus admitted to Langfang People's Hospital from October 2021 to March 2023 were prospectively selected and randomly divided into control group and experimental group according to the random number table method, with 40 cases in each group. The control group was given routine management, and the experimental group was combined with the health nutrition model based on digital management on this basis. Both groups began to intervene after the diagnosis of gestational diabetes mellitus, and the intervention lasted until one week before the patient's expected delivery. Follow-up to delivery. The delivery mode, weight gain during pregnancy, insulin use during pregnancy, pregnancy outcome and neonatal outcome of the two groups were statistically analyzed. The blood glucose level of the two groups before and after intervention were compared.Results:The age of the control group was 20-31 (26.25 ± 1.98) years old, while the age of the experimental group was 20-33 (25.93 ± 2.11) years old. The cesarean section rate in the experimental group was 32.50% (13/40), which was lower than that in the control group (57.50%, 23/40) ( χ2=5.05, P<0.05). The weight gain during pregnancy in the experimental group was (6.03 ± 0.89) kg, which was lower than (7.93 ± 1.36) kg in the control group ( t=7.39, P<0.05). After intervention, the levels of serum glycosylated hemoglobin, fasting blood glucose and 2 h postprandial blood glucose in the two groups were lower than those before intervention, and those in the experimental group were (5.19 ± 0.54) %, (4.98 ± 0.49) mmol/L, (6.04 ± 1.15) mmol/L, which were lower than (5.67 ± 0.75) %, (5.37 ± 0.54) mmol/L, (7.16 ± 1.36) mmol/L in the control group ( t=3.28, 3.42, 3.96, all P<0.05). The proportion of macrosomia in the experimental group was 2.50% (1/40), which was lower than 20.00% (8/40) in the control group ( χ2=4.51, P<0.05). The experimental group had no abnormal pregnancy outcome and neonatal outcome respectively as 92.50% (37/40) and 92.50% (37/40), which were 70.00% (28/40) and 57.50% (23/40) in the control group, their difference was statistically significant ( χ2=6.65, 13.07, both P<0.05). Conclusions:The application of health nutrition model based on digital management to intervene in patients with gestational diabetes could effectively improve their blood glucose control effect, improve pregnancy outcome and neonatal outcome, and hada good intervention effect.

Thyroid-associated ophthalmopathy(TAO) is an organ-specific autoimmune disease closely related to hy-perthyroidism,but it may also be detected in chronic autoimmune thyroiditis patients with normal thyroid functions or hy-pothyroidism.The clinical manifestations of TAO are diverse,and the progression of the disease varies.Therefore,accu-rately assessing its activity and the extent of lesions is crucial for guiding clinical treatment.Imaging examinations play an important role in the clinical diagnosis and evaluation of TAO.Commonly used methods include computed tomography(CT)and magnetic resonance imaging(MRI).In this article,the current applications of CT and MRI to the evaluation of TAO were reviewed,aiming to assist clinicians in selecting appropriate imaging modalities for accurate disease staging and formulating more rational treatment strategies.

In July 2024,the Diagnosis Related Groups(DRG)2.0 is released based on the Notice from the National Healthcare Security Administration on Issuing the DRG 2.0 and Deepening the Relevant Work.Compared with DRG 1.1,version 2.0 was established based on a wider range of suggestions regarding the Adjacent Diagnosis Related Groups(ADRG),Major Comorbidity or Complication(MCC),and Comorbidity or Complication(CC)from various institutions.A list of disease diagnoses and surgical operations that are not used as grouping rules was compiled,and grouping efficacy was further improved by upgrading the algorithms for MCC and CC with the help of AI.Meanwhile,it is necessary to pay more attention to the number of cases of ADRG,the better methods to list the MCC/CC,the suggestions of various doctors and continuously standardize the data and update the grouping scheme of DRG.