Journal of International Medical Research最新文献

ObjectivesThis study aimed to develop a multimodal predictive model that integrates clinical data, radiomics, and three-dimensional deep learning to forecast acute respiratory distress syndrome in patients with acute pancreatitis.MethodsThis retrospective study analyzed data from 759 patients with acute pancreatitis treated at three hospitals. Radiomics features were extracted from three-dimensional computed tomography images, and a three-dimensional deep learning model was developed using convolutional networks. These components were combined with clinical data using the XGBoost algorithm to construct a multimodal model. The performance of the model was compared with that of single-modal models and traditional scoring systems (Modified Computed Tomography Severity Index, Ranson score, and Bedside Index for Severity in Acute Pancreatitis), using area under the curve as the primary metric. Model interpretability was enhanced using variable importance analysis, SHapley Additive exPlanations, local interpretable model-agnostic explanations, calibration plots, and decision curve analysis.ResultsThe multimodal model achieved area under the curve values of 0.872 (training set) and 0.876 (test set), outperforming traditional scores (Modified Computed Tomography Severity Index: 0.747 and 0.759; Ranson score: 0.575 and 0.568; and Bedside Index for Severity in Acute Pancreatitis: 0.748 and 0.757, respectively) and single-modal models (radiomics: 0.638 and 0.727 and deep learning: 0.756 and 0.727, respectively).ConclusionBy integrating clinical tabular data, radiomics, and deep learning features, the multimodal model can predict the risk of acute respiratory distress syndrome in patients with acute pancreatitis at an early stage.

ObjectiveThis study aimed to evaluate the clinical outcomes of a new implant restoration approach for patients with limited interocclusal space in the posterior region.Materials and methodsA total of 37 patients (19 females and 18 males) were enrolled in this study, receiving 62 implants. A locking taper implant (Bicon) was placed during the first-stage procedure, positioned >2 mm under the bone. An extraoral cementation technique was used to cement the crown and abutment. The survival and success rates of the implant restorations were evaluated based on plaque index, modified bleeding index, probing depth, marginal bone loss, and the patient's subjective satisfaction.ResultsThe success rate of the new implant restorations was 96.7% at the 1-year follow-up, with patients reporting satisfactory outcomes. Marginal bone loss and soft tissue changes were not significantly different at different depths of placement (p > 0.05).ConclusionsThe use of a locking taper implant along with appropriate prosthetic strategies may be an effective approach for the prosthetic rehabilitation of patients with limited interocclusal space and reduced occlusal vertical dimensions.

Hypoparathyroidism is a rare endocrine condition characterized by insufficient secretion of parathyroid hormone (PTH), resulting in abnormally low calcium levels (hypocalcemia) and elevated phosphate levels (hyperphosphatemia) in the blood. This report describes a man in his late 30s with a chronic skin condition marked by dryness and desquamation. He occasionally experienced mild perioral numbness. Over the past year, he developed recurrent neuromuscular irritability, including worsening perioral numbness, tingling or numbness in the hands and feet, and muscle spasms consistent with tetany. He was diagnosed with hypoparathyroidism, and his symptoms improved markedly after calcium and calcitriol supplementation. Genetic testing revealed a novel heterozygous c.2298C>G (p. Tyr766Ter) mutation in exon 18 of the fibroblast growth factor receptor 1 gene. This case report aimed to describe this novel mutation and its potential role in the pathogenesis of primary hypoparathyroidism and to discuss relevant diagnostic and therapeutic management strategies. In addition, it broadens our understanding of genetic mutations associated with hypoparathyroidism and provides clinically relevant diagnostic information that may benefit future patients with the similar genetic alteration. Furthermore, it underscores the importance of genetic analysis in elucidating the heterogeneity and complexity of hypoparathyroidism, thereby supporting the development of more precise and tailored treatment approaches.

ObjectiveAlzheimer's disease and mild cognitive impairment involve brain atrophy, but neurotransmitter changes and their clinical implications are not well defined. This study aimed to examine the relationship between gray matter atrophy and neurotransmitter distributions and to build machine-learning models using gray matter-neurotransmitter co-localization as features.MethodsAmong 262 participants from the Alzheimer's Disease Neuroimaging Initiative (140 with Alzheimer's disease, 50 with mild cognitive impairment, and 72 controls), we used structural magnetic resonance imaging and voxel-based morphometry (family-wise error < 0.05), and JuSpace toolbox was used to assess the spatial correlation between gray matter atrophy and 13 neurotransmitter maps. We applied a train/validation/fixed test split (the test set was never used for selection or training); features were screened by univariate regression and least absolute shrinkage and selection operator regression, and models trained with nested 10-fold cross-validation were evaluated by the area under the receiver operating characteristic curve.ResultsBoth Alzheimer's disease and mild cognitive impairment showed gray matter loss in temporal, frontal, and cingulate areas. Atrophy was correlated with serotonergic, dopaminergic, and glutamatergic systems (false-discovery rate < 0.05). In mild cognitive impairment, reduced metabotropic glutamate receptor 5/μ-opioid receptor-gray matter correlation was associated with higher depression scores (r = -0.44, p = 0.001; r = -0.44, p = 0.001). The Random Forest model achieved an area under the receiver operating characteristic curve of 0.821, and Shapley additive explanations analysis confirmed key feature contributions.ConclusionNeurotransmitter-linked gray matter changes contribute to the pathology of Alzheimer's disease and mild cognitive impairment. The machine-learning model accurately differentiates these conditions, suggesting its utility for early diagnosis and disease staging.

ObjectiveTo describe patients with severe asthma treated with or eligible for monoclonal antibodies, assessing the health and economic burden using the Italian National Healthcare Service (SSN) administrative data.MethodsFrom 4.6 million inhabitants, among patients with asthma from 1 January to 31 December 2022, those with severe asthma were identified by monoclonal antibody dispensation (cohort A) and by eligibility for monoclonal antibodies, defined as continuous treatment with medium- or high-dose inhaled corticosteroids and long-acting beta agonists and the occurrence of exacerbations (cohort B-narrow and cohort B-broad according to "narrow" and" broad" definitions, respectively). One-year exacerbations, healthcare utilization, and direct costs were assessed.ResultsOf the 128,621 patients with asthma (51.8% women; mean age, 54 years), patients with severe asthma were identified as follows: cohort A (n = 3046; 2.4%), cohort B-narrow (n = 3517; 2.7%), and cohort B-broad (n = 7621; 5.6%). Compared with cohort A, patients in cohorts B-narrow and B-broad were older, had more comorbidities, experienced more moderate/severe exacerbations (70.9%-57.3% vs. 46.7%), and had higher hospitalization rates and greater drug use but fewer specialist visits. The annual SSN costs averaged €7512 for cohort A versus €2911-€2351 for cohorts B-narrow and B-broad. Cohort A incurred higher costs for asthma drugs, whereas cohorts B-narrow and B-broad incurred higher costs for concomitant drugs, hospitalizations, and specialist care.ConclusionsA significant disease burden exists in patients with uncontrolled severe asthma who are potentially eligible for monoclonal antibodies in Italy.

ObjectiveTo analyze the clinical features, diagnosis, and treatment of cerebral arteriovenous malformation with arterial stenosis and moyamoya disease.MethodsA 50-year-old man with a 20-year history of hypertension and multiple cerebral infarctions was admitted following the detection of cerebral artery stenosis. Imaging revealed a right posterior communicating artery aneurysm, severe stenosis of the right internal carotid artery (C6 segment), bilateral middle cerebral artery occlusion, and moyamoya vessels. The diagnosis was confirmed by cerebral angiography. The patient underwent stent-assisted aneurysm coiling and carotid stenting with balloon angioplasty.ResultsPostoperative antiplatelet, antihypertensive, and lipid-lowering therapies were administered. The patient recovered well without new neurological deficits and was discharged in stable condition. Follow-up assessment is ongoing.ConclusionDigital subtraction angiography remains the gold standard for diagnosing such complex cerebrovascular diseases. Endovascular intervention combined with medication can improve clinical outcomes, and regular follow-up assessment is essential to prevent complications.

Pleomorphic hyalinizing angiectatic tumor of soft tissue (PHAT) is a rare, low-grade neoplasm of uncertain lineage that predominantly affects the lower limbs of middle-aged adults. We report the case of a male in his early 20s who presented with a progressively enlarging, mildly tender mass above the right knee, present for 6 years. Imaging studies revealed a well-circumscribed lesion with obvious contrast enhancement on magnetic resonance imaging and marked internal vascularity on color Doppler ultrasound, located anterior to the distal quadriceps femoris. Extended excision was performed, and histopathologic examination confirmed the diagnosis of pleomorphic hyalinizing angiectatic tumor of soft tissue with negative surgical margins (R0 resection). The patient remained disease-free at the short-term follow-up (as of mid-2025). This report highlights the clinicopathologic features that differentiate pleomorphic hyalinizing angiectatic tumor of soft tissue from mimics such as myofibroma and intramuscular hemangioma, particularly in younger patients and at unusual anatomical locations.

ObjectiveThis study aimed to investigate the associations between fine particulate matter and its major chemical components and cognitive function among middle-aged and older adults in China.MethodsWe conducted a nationwide prospective cohort study using data from the China Health and Retirement Longitudinal Study (2011-2018). Cognitive function was repeatedly assessed through standardized tests of memory and mental status. Annual average concentrations of fine particulate matter and its five major components (sulfate, nitrate, ammonium, black carbon, and organic matter) were estimated at the city level. Fixed-effects models and restricted cubic spline analyses were used to evaluate associations, and random forest models were used to rank the relative importance of components.ResultsHigher exposure to fine particulate matter and several of its major components was significantly associated with lower cognitive scores. Among these components, sulfate exhibited the strongest adverse association with cognitive function. The findings were consistent across multiple sensitivity analyses, including those restricted to provincial capitals and those adjusting for potential confounders.ConclusionsExposure to fine particulate matter and its chemical components may contribute to cognitive impairment among middle-aged and older adults in China. Sulfate appears to be particularly detrimental. These results highlight the need for targeted air pollution control policies that address specific fine particulate matter components to mitigate the burden of cognitive impairment.

PurposeFirst-phase ejection fraction has been established as an innovative indicator for detecting early left ventricular systolic changes. This study aimed to use first-phase ejection fraction to assess early changes in the left ventricular systolic function in patients with cirrhosis.MethodsIn this cross-sectional study, 122 patients with cirrhosis were stratified into the following groups based on Child-Turcotte-Pugh scores: group B1 (Child-Turcotte-Pugh class A, n = 35), group B2 (Child-Turcotte-Pugh class B, n = 43), and group B3 (Child-Turcotte-Pugh class C, n = 44). Thirty-nine healthy controls (group A) were enrolled for comparison. Basic information, clinical biochemical parameters, and ultrasound parameters were collected.ResultsCompared with groups A, B1, and B2, group B3 showed significantly decreased left ventricular ejection fraction (all p < 0.05). Groups B2 and B3 showed significantly decreased absolute values of global longitudinal strain compared with groups A and B1 (all p < 0.05). Compared with group B2, group B3 demonstrated more pronounced decrease in absolute values of global longitudinal strain (p < 0.05). First-phase ejection fraction was significantly increased in group B1 compared with that in group A (p < 0.05). In contrast, first-phase ejection fraction was significantly decreased in groups B2 and B3, with group B3 showing a further significant decrease compared with group B2 (p < 0.05). First-phase ejection fraction, ejection fraction, and global longitudinal strain demonstrated significant negative correlations with Child-Turcotte-Pugh class (r = -0.619, -0.429, and -0.608, respectively; p < 0.05). The prevalence of cirrhotic cardiomyopathy was significantly higher in group B3 than in group B1. The area under the receiver operating characteristic curve for first-phase ejection fraction in diagnosing cirrhotic cardiomyopathy was superior to that for ejection fraction and global longitudinal strain.ConclusionFirst-phase ejection fraction sensitively detects early alterations in left ventricular systolic function in cirrhotic patients and may facilitate early identification of cirrhotic cardiomyopathy.

ObjectiveThe purpose of this study was to assess whether circulating soluble ST2 independently predicts prognosis in patients with chronic heart failure.MethodsThis study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) under the unique registration number CRD42023489018. Two researchers systematically searched PubMed, Embase, and Web of Science for all studies published up to 1 September 2024. To evaluate the quality of the study, the Newcastle-Ottawa Scale was used; Review Manager software was used for statistical analysis and construction of forest plots.ResultsThe final analysis comprised 17 studies in total. This meta-analysis demonstrated that a high soluble ST2 level was a predictor of poor all-cause mortality (hazard ratio: 1.03, 95% confidence interval: 1.02-1.04, p < 0.00001), poor all-cause mortality/heart failure-related readmission (hazard ratio: 1.46, 95% confidence interval: 1.33-1.61, p < 0.00001), and higher cardiovascular mortality/heart failure-related hospitalization (hazard ratio: 1.50, 95% confidence interval: 1.30-1.74, p < 0.00001) in patients with chronic heart failure. Subgroup analyses were conducted based on ethnicity, sex, left ventricular ejection fraction, and follow-up duration for both all-cause mortality and all-cause mortality/heart failure-related readmission. Soluble ST2 demonstrated good prognostic value in all subgroups.ConclusionThis study, based on current evidence, suggests that soluble ST2 has independent prognostic value in patients with chronic heart failure. The soluble ST2 biomarker performed well in predicting all-cause mortality/heart failure-related readmission and cardiovascular mortality/heart failure-related hospitalization. Further research is needed to validate its role in clinical practice.