Urolithiasis represents a prevalent clinical challenge marked by high recurrence rates and morbidity，with existing preventive strategies struggling to effectively curb its epidemic trajectory，thereby posing a significant threat to public health. The etiology of this condition is intricate，involving a complex network of interactions spanning classical supersaturation-crystallization theory，Randall’s plaque theory，and multifactorial elements such as cellular injury，inflammatory responses，metabolic derangements，the gut-kidney axis，immune dysregulation，and genetic predisposition. However，the critical mechanisms initiating stone formation and the early pathophysiological processes remain incompletely elucidated，constituting the core impasse in current preventive strategies. This review systematically synthesizes classical theories and cutting-edge advancements in urolithiasis etiology research，emphasizing the urgent need to integrate emerging technologies，including high-dimensional omics，advanced imaging modalities，and artificial intelligence，to dissect pivotal pathological nodes in early stone formation. Such interdisciplinary efforts are essential to overcome cognitive bottlenecks and ultimately achieve personalized，precision-based prevention strategies.

Objective To analyze the differences in bone serum protein between patients with osteoporosis accompanied by obstructive sleep apnea syndrome(OSAS)and those with osteoporosis only using proteomics.Methods A total of 80 osteoporosis patients who attended our hospital between June 2022 and June 2024 were enrolled.Based on their polysomnography results,the participants were divided into an OSAS and osteoporosis comorbidity(OSAS-osteoporosis)group(n=42)and an osteoporosis only group(n=38).Propensity score matching was applied to incorporate covariates in logistic regression so that the individual characteristics of the two groups of patients were generally balanced.Following the matching procedure,a final cohort of 20 matched pairs was obtained and subsequently utilized for further analysis.The mass spectrum was obtained using laser desorption ionization mass spectrometry.Principal component analysis(PCA)was performed to assess differences in metabolic patterns between groups.Partial least squares discriminant analysis(PLS-DA)and orthogonal PLS-DA(OPLS-DA)were employed for further data analysis.Variable importance in projection(VIP)scores of each substance were calculated with OPLS-DA to screen the metabolites showing inter-group differences.Heatmaps were generated to visualize metabolic profile differences between the OSAS-osteoporosis group and the osteoporosis group.Enrichment pathway analysis was conducted on the differential identified metabolites.Results After propensity score matching,individual characteristics between the groups were well balanced.Mass spectrometry revealed significant differences between the OSAS-osteoporosis and osteoporosis groups.In the PCA score plot,the separation trend of the two groups was not significant.The PLS-DA score plot showed a discernible separation trend,with R2 and Q2 lower than those of the corresponding results of the real model,confirming the reliability of the model.OPLS-DA showed that the total R2X of the model was 0.635,R2Y was 0.879,and O2Y was 0.728,showing obvious separation trends between the two groups.A total of 16 differential metabolites were identified,including stearyl-oleyl-glycerol phosphate choline,phosphate choline,L-histidine,erucamide,2'-deoxyuridine,1-palmitoyl glycerol,thymine,tyramine,L-pyroglutamic acid,L-glutamic acid,myristate,glycerol-3-phosphate,caprylic acid,pregnenolone,L-arginine,D-4-hydroxyphenylglycine,and isobutyric acid.Heatmaps showed significant differences in metabolic profiles between the OSAS-osteoporosis group and the osteoporosis group.Pathway enrichment analysis showed that 27 metabolic pathways were involved.27 metabolic pathways.Under the conditions of P<0.05 and pathway impact>0.2,the three most significant metabolic pathways identified included mainly alanine,aspartate,and glutamate metabolism,arginine biosynthesis,and histidine metabolism.Conclusion Significant differences were observed in the metabolic profiles between patients with both OSAS and osteoporosis and those with osteoporosis alone.

Objective:To investigate the impact of deep learning image reconstruction (DLIR), adaptive statistical iterative reconstruction-veo (ASiR-V) and filtered back projection (FBP) on the histogram and wavelet features of portal venous phase abdominal CT in patients with hepatic tumor.Methods:The CT data of 68 patients with hepatic tumor who underwent enhanced CT scans were retrospectively collected. FBP, 30%ASiR-V, DLIR-L, DLIR-M and DLIR-H images were reconstructed. The images of portal venous phase were reconstructed with five algorithms, including FBP, ASIR-V at a level of 30% (ASiR-V 30%), DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Histogram and wavelet features were extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle, and compared using one-way analysis of variance or Kruskal-Wallis test. Two radiologists delineated the three-dimensional lesions independently and one of them repeated the delineation after one month. Intra-class correlation coefficients ( ICC) among five sets of images were calculated to evaluate the consistency of radiomics features of hepatic lesion. P<0.05 was considered to indicate statistical significance. Results:Most histogram and wavelet features extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle showed significant differences among five groups (all P<0.05). The number of features without significant differences decreased with the intensity of DLIR reconstruction increased. For histogram features, there were no significant differences of energy, mean, median, and total energy among five sets of images ( P>0.05). For wavelet features, there were no significant differences of mean and median among five sets of images ( P>0.05). The consistency of all histogram features was high except for the mean value of wavelet feature. The intra-and inter-observer ICC ranged from 0.756 to 1 and 0.767 to 1, respectively. Conclusion:Both 30%ASiR-V and DLIR at three levels algorithms had influence on the histogram and wavelet features of abdominal organs and hepatic tumors extracted from CT images in portal venous phase, and the effects expanded with the strengthening of levels. Median can be a reliable quantitative parameter for CT texture analysis of hepatic tumor.

Urolithiasis represents a prevalent clinical challenge marked by high recurrence rates and morbidity，with existing preventive strategies struggling to effectively curb its epidemic trajectory，thereby posing a significant threat to public health. The etiology of this condition is intricate，involving a complex network of interactions spanning classical supersaturation-crystallization theory，Randall’s plaque theory，and multifactorial elements such as cellular injury，inflammatory responses，metabolic derangements，the gut-kidney axis，immune dysregulation，and genetic predisposition. However，the critical mechanisms initiating stone formation and the early pathophysiological processes remain incompletely elucidated，constituting the core impasse in current preventive strategies. This review systematically synthesizes classical theories and cutting-edge advancements in urolithiasis etiology research，emphasizing the urgent need to integrate emerging technologies，including high-dimensional omics，advanced imaging modalities，and artificial intelligence，to dissect pivotal pathological nodes in early stone formation. Such interdisciplinary efforts are essential to overcome cognitive bottlenecks and ultimately achieve personalized，precision-based prevention strategies.

Objective:To investigate the impact of deep learning image reconstruction (DLIR), adaptive statistical iterative reconstruction-veo (ASiR-V) and filtered back projection (FBP) on the histogram and wavelet features of portal venous phase abdominal CT in patients with hepatic tumor.Methods:The CT data of 68 patients with hepatic tumor who underwent enhanced CT scans were retrospectively collected. FBP, 30%ASiR-V, DLIR-L, DLIR-M and DLIR-H images were reconstructed. The images of portal venous phase were reconstructed with five algorithms, including FBP, ASIR-V at a level of 30% (ASiR-V 30%), DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Histogram and wavelet features were extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle, and compared using one-way analysis of variance or Kruskal-Wallis test. Two radiologists delineated the three-dimensional lesions independently and one of them repeated the delineation after one month. Intra-class correlation coefficients ( ICC) among five sets of images were calculated to evaluate the consistency of radiomics features of hepatic lesion. P<0.05 was considered to indicate statistical significance. Results:Most histogram and wavelet features extracted from hepatic lesion, liver, spleen, kidney and erector spinae muscle showed significant differences among five groups (all P<0.05). The number of features without significant differences decreased with the intensity of DLIR reconstruction increased. For histogram features, there were no significant differences of energy, mean, median, and total energy among five sets of images ( P>0.05). For wavelet features, there were no significant differences of mean and median among five sets of images ( P>0.05). The consistency of all histogram features was high except for the mean value of wavelet feature. The intra-and inter-observer ICC ranged from 0.756 to 1 and 0.767 to 1, respectively. Conclusion:Both 30%ASiR-V and DLIR at three levels algorithms had influence on the histogram and wavelet features of abdominal organs and hepatic tumors extracted from CT images in portal venous phase, and the effects expanded with the strengthening of levels. Median can be a reliable quantitative parameter for CT texture analysis of hepatic tumor.

Humans ; Aged ; Lung Diseases ; Pneumonia/drug therapy* ; Adrenal Cortex Hormones/therapeutic use* ; Pulmonary Disease, Chronic Obstructive/drug therapy* ; Administration, Inhalation ; Community-Acquired Infections/drug therapy*

BACKGROUND:Hydroxyapatite is the main inorganic component of bone tissue.The polymer has the structure and function of a biomimetic extracellular matrix.The composites of hydroxyapatite and polymer have been widely studied. OBJECTIVE:To summarize the research status of hydroxyapatite composite polymer materials for bone tissue repair. METHODS:The articles collected in PubMed,Web of Science,CNKI and WanFang databases were searched from January 2010 to April 2023.The Chinese and English search terms were"hydroxyapatite,polymer,composites,degradability,bone defect,bone repair".Finally,75 articles were included for review. RESULTS AND CONCLUSION:Polymers often used in composite with hydroxyapatite for bone tissue repair include natural polymers(collagen,chitosan,alginate,serine protein,cellulose,hyaluronic acid,and polyhydroxybutyrate)and synthetic polymers[polylactic acid,polylactic acid-hydroxyacetic acid copolymer,poly(has-lactide),poly(amino acid)and poly(vinyl alcohol)].The mechanical properties and osteoinductivity of hydroxyapatite/polymer composites were improved compared with pure hydroxyapatite.Hydroxyapatite composite with polymers can be made into porous scaffolds,hydrogels,and coatings for bone repair.Hydroxyapatite/polymer composites can accelerate bone reconstruction with a slow release of loaded drugs and cytokines due to their bionic extracellular matrix structure and function.Based on the diversity of causes of bone defects and the fact that bone repair is a complex continuous process involving multiple biological factors and proteins,repair materials with mechanical properties matching bone tissue,degradation processes synchronized with bone repair,and efficient osteogenesis and vascularization need to be further investigated.

Objective:To report the long-term outcomes of Chinese rectal cancer patients after adopting a Watch and Wait (W&W) strategy following neoadjuvant therapy (NAT).Methods:This multicenter, cross-sectional study was based on real-world data. The study cohort comprised rectal cancer patients who had achieved complete or near complete clinical responses (cCRs, near-cCRs) after NAT and were thereafter managed by a W&W approach, as well as a few patients who had achieved good responses after NAT and had then undergone local excision for confirmation of pathological complete response. All participants had been followed up for ≥2 years. Patients with distant metastases at baseline or who opted for observation while living with the tumor were excluded. Data of eligible patients were retrospectively collected from the Chinese Wait-and-Watch Data Collaboration Group database. These included baseline characteristics, type of NAT, pre-treatment imaging results, evaluation of post-NAT efficacy, salvage measures, and treatment outcomes. We herein report the long-term outcomes of Chinese rectal cancer patients after NAT and W&W and the differences between the cCR and near-cCR groups.Results:Clinical data of 318 rectal cancer patients who had undergone W&W for over 2 years and been followed up were collected from eight medical centers (Peking University Cancer Hospital, Fudan University Shanghai Cancer Center, Sun Yat-sen University Cancer Center, Shanghai Changhai Hospital, Peking Union Medical College Hospital, Liaoning Cancer Hospital, the First Hospital of Jilin University, and Yunnan Cancer Hospital.) The participants comprised 221 men (69.4%) and 107 women (30.6%) of median age 60 (26-86) years. The median distance between tumor and anal verge was 3.4 (0-10.4) cm. Of these patients, 291 and 27 had achieved cCR or near-cCR, respectively, after NAT. The median duration of follow-up was 48.4 (10.2-110.3) months. The 5-year cumulative overall survival rate was 92.4% (95%CI: 86.8%-95.7%), 5-year cumulative disease-specific survival (CSS) rate 96.6% (95%CI: 92.2%-98.5%), 5-year cumulative organ-preserving disease-free survival rate 86.6% (95%CI: 81.0%-90.7%), and 5-year organ preservation rate 85.3% (95%CI: 80.3%-89.1%). The overall 5-year local recurrence and distant metastasis rates were 18.5% (95%CI: 14.9%-20.8%) and 8.2% (95%CI: 5.4%-12.5%), respectively. Most local recurrences (82.1%, 46/56) occurred within 2 years, and 91.0% (51/56) occurred within 3 years, the median time to recurrence being 11.7 (2.5-66.6) months. Most (91.1%, 51/56) local recurrences occurred within the intestinal lumen. Distant metastases developed in 23 patients; 60.9% (14/23) occurred within 2 years and 73.9% (17/23) within 3 years, the median time to distant metastasis being 21.9 (2.6-90.3) months. Common sites included lung (15/23, 65.2%), liver (6/23, 26.1%), and bone (7/23, 30.4%) The metastases involved single organs in 17 patients and multiple organs in six. There were no significant differences in overall, cumulative disease-specific, or organ-preserving disease-free survival or rate of metastases between the two groups (all P>0.05). The 5-year local recurrence rate was higher in the near-cCR than in the cCR group (41.6% vs. 16.4%, P<0.01), with a lower organ preservation rate (69.2% vs. 88.0%, P<0.001). The success rates of salvage after local recurrence and distant metastasis were 82.1% (46/56) and 13.0% (3/23), respectively. Conclusion:Rectal cancer patients who achieve cCR or near-cCR after NAT and undergo W&W have favorable oncological outcomes and a high rate of organ preservation. Local recurrence and distant metastasis during W&W follow certain patterns, with a relatively high salvage rate for local recurrence. Our findings highlight the importance of close follow-up and timely intervention during the W&W process.

Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.