DOAJ (DOAJ: Directory of Open Access Journals) Framed by the Technology Acceptance Model (TAM), this study treats perceived ease of use and perceived usefulness as mediators and, following Williams' creativity theory, divides professional creativity into curiosity, imagination, risk-taking, and challenge; using a structured questionnaire with leather product designers and testing via confirmatory factor analysis and structural equation modeling, it finds that AI application has a significant positive influence on all four creativity dimensions, that both perceived ease of use and perceived usefulness significantly mediate the curiosity, imagination, and challenge dimensions, and that for risk-taking only perceived ease of use is a significant mediator while the effect of perceived usefulness is not statistically significant.
Framed by the Technology Acceptance Model (TAM), this study treats perceived ease of use and perceived usefulness as mediators and, following Williams' creativity theory, divides professional creativity into curiosity, imagination, risk-taking, and challenge; using a structured questionnaire with leather product designers and testing via confirmatory factor analysis and structural equation modeling, it finds that AI application has a significant positive influence on all four creativity dimensions, that both perceived ease of use and perceived usefulness significantly mediate the curiosity, imagination, and challenge dimensions, and that for risk-taking only perceived ease of use is a significant mediator while the effect of perceived usefulness is not statistically significant.
Framed by the Technology Acceptance Model (TAM), this study treats perceived ease of use and perceived usefulness as mediators and, following Williams' creativity theory, divides professional creativity into curiosity, imagination, risk-taking, and challenge; using a structured questionnaire with leather product designers and testing via confirmatory factor analysis and structural equation modeling, it finds that AI application has a significant positive influence on all four creativity dimensions, that both perceived ease of use and perceived usefulness significantly mediate the curiosity, imagination, and challenge dimensions, and that for risk-taking only perceived ease of use is a significant mediator while the effect of perceived usefulness is not statistically significant.
Framed by the Technology Acceptance Model (TAM), this study treats perceived ease of use and perceived usefulness as mediators and, following Williams' creativity theory, divides professional creativity into curiosity, imagination, risk-taking, and challenge; using a structured questionnaire with leather product designers and testing via confirmatory factor analysis and structural equation modeling, it finds that AI application has a significant positive influence on all four creativity dimensions, that both perceived ease of use and perceived usefulness significantly mediate the curiosity, imagination, and challenge dimensions, and that for risk-taking only perceived ease of use is a significant mediator while the effect of perceived usefulness is not statistically significant.
European Journal Pharmaceutical and Medical Research Using celecoxib as the model drug, this study applied a 2^3 factorial design to vary gelling agent type (Carbopol 934 vs. HPMC), liquid paraffin concentration (5.0% vs. 7.5% w/w), and emulsifier blend concentration (Tween 20/Span 20 at 1.5% vs. 2.5% w/w), prepared and characterized eight batches (F1–F8) of topical emulgel, and identified F6 (2.5% HPMC, 5% liquid paraffin, 2.5% surfactant) as optimal, with 93.2±1.2% cumulative in vitro release over 36 hours versus 45% for the standard formulation, release kinetics best fitted by the Higuchi diffusion model (R2=0.996), ex-vivo porcine skin cumulative transdermal delivery of 75.6±1.9% (4267±107 μg/cm2) with cutaneous retention of 12.8±1.
Using celecoxib as the model drug, this study applied a 2^3 factorial design to vary gelling agent type (Carbopol 934 vs. HPMC), liquid paraffin concentration (5.0% vs. 7.5% w/w), and emulsifier blend concentration (Tween 20/Span 20 at 1.5% vs. 2.5% w/w), prepared and characterized eight batches (F1–F8) of topical emulgel, and identified F6 (2.5% HPMC, 5% liquid paraffin, 2.5% surfactant) as optimal, with 93.2±1.2% cumulative in vitro release over 36 hours versus 45% for the standard formulation, release kinetics best fitted by the Higuchi diffusion model (R2=0.996), ex-vivo porcine skin cumulative transdermal delivery of 75.6±1.9% (4267±107 μg/cm2) with cutaneous retention of 12.8±1.
Using celecoxib as the model drug, this study applied a 2^3 factorial design to vary gelling agent type (Carbopol 934 vs. HPMC), liquid paraffin concentration (5.0% vs. 7.5% w/w), and emulsifier blend concentration (Tween 20/Span 20 at 1.5% vs. 2.5% w/w), prepared and characterized eight batches (F1–F8) of topical emulgel, and identified F6 (2.5% HPMC, 5% liquid paraffin, 2.5% surfactant) as optimal, with 93.2±1.2% cumulative in vitro release over 36 hours versus 45% for the standard formulation, release kinetics best fitted by the Higuchi diffusion model (R2=0.996), ex-vivo porcine skin cumulative transdermal delivery of 75.6±1.9% (4267±107 μg/cm2) with cutaneous retention of 12.8±1.
Using celecoxib as the model drug, this study applied a 2^3 factorial design to vary gelling agent type (Carbopol 934 vs. HPMC), liquid paraffin concentration (5.0% vs. 7.5% w/w), and emulsifier blend concentration (Tween 20/Span 20 at 1.5% vs. 2.5% w/w), prepared and characterized eight batches (F1–F8) of topical emulgel, and identified F6 (2.5% HPMC, 5% liquid paraffin, 2.5% surfactant) as optimal, with 93.2±1.2% cumulative in vitro release over 36 hours versus 45% for the standard formulation, release kinetics best fitted by the Higuchi diffusion model (R2=0.996), ex-vivo porcine skin cumulative transdermal delivery of 75.6±1.9% (4267±107 μg/cm2) with cutaneous retention of 12.8±1.
Cancer Discovery The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
The authors built MutationProjector, a graph-attention cancer-genome foundation model integrating eight molecular network types, pretrained by masked gene reconstruction on genetic alterations from 30,328 tumors across 10 solid cancer types, then transfer-learned with a random forest on small labeled cohorts; it stratified survival in immunotherapy response prediction with hazard ratios of 0.71 for bladder, 0.74 for lung and 0.59 for melanoma, reached HR = 0.43 in a cisplatin-treated bladder cohort, and proposed KMT2D and SMARCA4-STK11 as candidate biomarkers via attention analysis.
发表出处待核验 Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
Starting from a previously trained temporal convolutional network sperm whale click-train detector, the study compared four transfer approaches across four sources (BAL, CS, ICE, MED)—cross-dataset baseline evaluation, training from scratch on only 500 target recordings, pretraining with random fine-tuning, and pretraining with active (uncertainty-based) fine-tuning—and found that pretrained models dropped in performance on unseen data but that fine-tuning with 500 target recordings effectively mitigated the drop, with active fine-tuning consistently outperforming the other approaches although its gain over random fine-tuning was marginal.
发表出处待核验 Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.
Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.
Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.
Using a three-round modified Delphi method, a 45-member panel of clinicians and researchers in diabetes, obesity and/or eating disorders plus people with lived experience reached an average 91.7% consensus on recommendations for GLP-1 receptor agonist use in eating disorder contexts, covering pre-treatment screening, risk-stratified monitoring, standardized educational materials and concurrent evidence-based psychotherapy, stating that these agents should generally be avoided in active eating disorders, particularly anorexia nervosa, atypical anorexia nervosa and bulimia nervosa with marked dietary restraint and/or significant weight suppression, and setting out research priorities in epidemiology, regulation and binge eating disorder trials.
American Journal of Respiratory Cell and Molecular Biology In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
In 22 C57BL/6 mice (11 given triple oropharyngeal bleomycin at 0.25 mg/kg to induce pulmonary fibrosis and 11 given saline), respiratory-gated µCT was acquired at baseline and days 7, 14, and 21, a U-Net deep-learning algorithm segmented the lungs into left and right lobes that were further divided into apical and caudal regions using airway landmarks for four subregions, and regional volumes, aeration compartments, and ventilation maps were extracted; saline-treated mice showed stable metrics with minimal interanimal variability, whereas bleomycin-treated animals showed early and heterogeneous fibrotic changes with the apical regions most affected and the caudal-right region displaying a compensatory functional increase, so that µCT-based regional analysis detected localized dysfunction a
Diabetes In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.
In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.
In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.
In 24,638 UK Biobank participants with prediabetes, LASSO and elastic net selection of nuclear magnetic resonance metabolomic markers yielded 16 biomarkers, and k-means clustering in the training set (12,322 participants) identified low-, intermediate-, and high-metabolic-risk subtypes that showed progressively higher risks of type 2 diabetes, cardiovascular disease, and chronic kidney disease in the validation set (12,316 participants), with diet-quality associations differing across subtypes and Mendelian randomization suggesting potential causal links for several metabolomic biomarkers.
Journal of Clinical Gastroenterology This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
This systematic review and meta-analysis pooled 5 studies (675 lesions; 2,685,674 cholangioscopic images) using PRISMA, MOOSE, and Cochrane Diagnostic Test Accuracy methodology with a bivariate model, and found that AI-assisted cholangioscopy—mostly deep learning systems using convolutional neural networks at roughly 30 to 60 frames per second—achieved a pooled sensitivity of 95% (95% CI: 85-98), specificity of 88% (95% CI: 76-94), and diagnostic accuracy (SROC) of 97% (95% CI: 95-98) for indeterminate or malignant biliary strictures, with a CNN-only sensitivity analysis (4 studies, 538 patients) showing consistent results, leading the authors to call the approach promising.
American Journal of Respiratory Cell and Molecular Biology Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h
Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h
Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h
Saavedra and colleagues used CITE-seq to profile circulating CD3+ T cells from 18 adults with cystic fibrosis before and after starting elexacaftor-tezacaftor-ivacaftor (ETI), finding CFTR modulation induces transcriptional reprogramming most pronounced in CD4+ memory T cells (upregulation of PNP and MT1X, enrichment of mitochondrial metabolism and oxidative phosphorylation pathways, and decline in chromatin remodeling and inflammatory activation genes), while Seahorse metabolic flux analysis, splenocytes from CFTR F508del homozygous mice, and CRISPR-Cas9 knockdown of CFTR in primary human lymphocytes all show reduced oxygen consumption, glycolytic capacity, and ATP production in CF lymphocytes, and changes in CD4+ memory T cell gene expression correlate with FEV1 improvement and reduced h
Journal of Orthopaedic Trauma In this single-center retrospective cohort of 197 geriatric patients who underwent fixation of OTA/AO 31-A/B/C hip fractures, an AI-driven Clinical Deterioration Index (CDI, 0–100 from 31 clinical measures) measured in the first 48 postoperative hours identified 15 patients (7.6%) with CDI ≥65 who had more complications (93.3% vs 26.4%), longer stays (10.1 vs 5.2 days), shorter discharge ambulation (8.8 vs 42.3 feet), and higher one-year mortality (20.0% vs 3.8%), while the institutional cutoff of 65 showed high specificity (99.3%) but low sensitivity (22.6%) and an optimized threshold of 47.7 raised sensitivity to 77.4% with comparable accuracy (75.0%).
In this single-center retrospective cohort of 197 geriatric patients who underwent fixation of OTA/AO 31-A/B/C hip fractures, an AI-driven Clinical Deterioration Index (CDI, 0–100 from 31 clinical measures) measured in the first 48 postoperative hours identified 15 patients (7.6%) with CDI ≥65 who had more complications (93.3% vs 26.4%), longer stays (10.1 vs 5.2 days), shorter discharge ambulation (8.8 vs 42.3 feet), and higher one-year mortality (20.0% vs 3.8%), while the institutional cutoff of 65 showed high specificity (99.3%) but low sensitivity (22.6%) and an optimized threshold of 47.7 raised sensitivity to 77.4% with comparable accuracy (75.0%).
In this single-center retrospective cohort of 197 geriatric patients who underwent fixation of OTA/AO 31-A/B/C hip fractures, an AI-driven Clinical Deterioration Index (CDI, 0–100 from 31 clinical measures) measured in the first 48 postoperative hours identified 15 patients (7.6%) with CDI ≥65 who had more complications (93.3% vs 26.4%), longer stays (10.1 vs 5.2 days), shorter discharge ambulation (8.8 vs 42.3 feet), and higher one-year mortality (20.0% vs 3.8%), while the institutional cutoff of 65 showed high specificity (99.3%) but low sensitivity (22.6%) and an optimized threshold of 47.7 raised sensitivity to 77.4% with comparable accuracy (75.0%).
In this single-center retrospective cohort of 197 geriatric patients who underwent fixation of OTA/AO 31-A/B/C hip fractures, an AI-driven Clinical Deterioration Index (CDI, 0–100 from 31 clinical measures) measured in the first 48 postoperative hours identified 15 patients (7.6%) with CDI ≥65 who had more complications (93.3% vs 26.4%), longer stays (10.1 vs 5.2 days), shorter discharge ambulation (8.8 vs 42.3 feet), and higher one-year mortality (20.0% vs 3.8%), while the institutional cutoff of 65 showed high specificity (99.3%) but low sensitivity (22.6%) and an optimized threshold of 47.7 raised sensitivity to 77.4% with comparable accuracy (75.0%).
发表出处待核验 This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
This review, using obsessive-compulsive disorder (OCD) as its example, surveys the advances and constraints of big data approaches to three questions—what causes the disorder, which treatments work best for whom, and how to widen access to evidence-based care—reporting that large-scale pooled data such as ENIGMA-OCD show some previously reported effects to be small but reproducible while others do not replicate at scale or appear to be medication effects, that the Global OCD Study harmonized methods across five international sites and recruited 250 unmedicated adults with OCD and 250 matched healthy volunteers and found relatively few case-control brain differences but more informative associations between specific brain and cognitive measures and OC clinical profiles, and that big data ar
Research Explorer (The University of Manchester) Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Addressing the assumption in European energy poverty research that drivers are separate and additive, this study develops a three-level modelling framework that operationalises intersectionality as a mediating structure linking socio-economic characteristics to energy poverty, and uses regional data for England with combined statistical and machine learning methods to identify underlying patterns, finding that similar levels of energy poverty can emerge from different combinations of conditions: a socio-economic gradient related to labour market position, education, and health plays a dominant role, while additional intersectional patterns capture life-stage differences and energy system characteristics, particularly heating types.
Dentomaxillofacial Radiology This phantom study acquired lateral cephalometric radiographs of an adult dry human skull phantom at the routine dose (80 kVp, 200 mA, 100 ms; DAP 270 mGy cm2) and at 60%, 40%, 20%, and 10% of that dose, applied two levels of non-artificial-intelligence sharpening with the Medical Image Enhancer software (mild: sharpening strength 55%, contrast ratio 20%; marked: 70%/70%), and had six reviewers rate the visibility of five reference points and soft tissue using Scheffe's method of paired comparisons with ordinal logistic regression odds ratios relative to the 100%-dose image; mildly sharpened 60%-dose images gave ORs above 1.
This phantom study acquired lateral cephalometric radiographs of an adult dry human skull phantom at the routine dose (80 kVp, 200 mA, 100 ms; DAP 270 mGy cm2) and at 60%, 40%, 20%, and 10% of that dose, applied two levels of non-artificial-intelligence sharpening with the Medical Image Enhancer software (mild: sharpening strength 55%, contrast ratio 20%; marked: 70%/70%), and had six reviewers rate the visibility of five reference points and soft tissue using Scheffe's method of paired comparisons with ordinal logistic regression odds ratios relative to the 100%-dose image; mildly sharpened 60%-dose images gave ORs above 1.
This phantom study acquired lateral cephalometric radiographs of an adult dry human skull phantom at the routine dose (80 kVp, 200 mA, 100 ms; DAP 270 mGy cm2) and at 60%, 40%, 20%, and 10% of that dose, applied two levels of non-artificial-intelligence sharpening with the Medical Image Enhancer software (mild: sharpening strength 55%, contrast ratio 20%; marked: 70%/70%), and had six reviewers rate the visibility of five reference points and soft tissue using Scheffe's method of paired comparisons with ordinal logistic regression odds ratios relative to the 100%-dose image; mildly sharpened 60%-dose images gave ORs above 1.
This phantom study acquired lateral cephalometric radiographs of an adult dry human skull phantom at the routine dose (80 kVp, 200 mA, 100 ms; DAP 270 mGy cm2) and at 60%, 40%, 20%, and 10% of that dose, applied two levels of non-artificial-intelligence sharpening with the Medical Image Enhancer software (mild: sharpening strength 55%, contrast ratio 20%; marked: 70%/70%), and had six reviewers rate the visibility of five reference points and soft tissue using Scheffe's method of paired comparisons with ordinal logistic regression odds ratios relative to the 100%-dose image; mildly sharpened 60%-dose images gave ORs above 1.
SMU Scholar (Southern Methodist University) This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.
This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.
This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.
This dissertation integrates multimodal retinal imaging, including fundus photography, OCT, and OCTA, to build a suite of deep learning frameworks spanning vessel segmentation networks, biomarker discovery pipelines, and multimodal fusion models for detecting early glaucomatous changes with high precision, robustness, and interpretability, and contributes several curated datasets including GSS-RetVein as standardized benchmarks for cross-domain validation, reporting that the proposed models outperform existing state-of-the-art approaches across multiple public and clinical datasets with marked improvements in diagnostic sensitivity and specificity.
发表出处待核验 Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.
Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.
Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.
Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.
DOAJ (DOAJ: Directory of Open Access Journals) The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
The work developed and characterized two deferoxamine-loaded topical formulations, a polymeric film and a hydrogel, based on Opuntia ficus-indica mucilage, aimed at wound-dressing applications.
American Journal of Respiratory and Critical Care Medicine Using a myeloid-specific Tsc2 deletion mouse model that recapitulates key features of human cardiac sarcoidosis, and integrating single-cell RNA sequencing, bioinformatic analyses, histopathology, in vitro functional studies in murine and human macrophages, and in vivo pharmacologic inhibition, the study found that constitutive mTORC1 activation induces a fibrogenic macrophage (FibMac) population in the heart characterized by Cd63, Spp1, Gpnmb, and Fabp5 expression and arising through a TGF-β-dependent monocyte-to-FibMac differentiation process, and identified MMP12 as a macrophage-intrinsic inducer and dominant effector of this program: recombinant MMP12 was sufficient to enforce fibrogenic differentiation, clustering, and epithelioid features in mouse and human macrophages, whereas selec
Using a myeloid-specific Tsc2 deletion mouse model that recapitulates key features of human cardiac sarcoidosis, and integrating single-cell RNA sequencing, bioinformatic analyses, histopathology, in vitro functional studies in murine and human macrophages, and in vivo pharmacologic inhibition, the study found that constitutive mTORC1 activation induces a fibrogenic macrophage (FibMac) population in the heart characterized by Cd63, Spp1, Gpnmb, and Fabp5 expression and arising through a TGF-β-dependent monocyte-to-FibMac differentiation process, and identified MMP12 as a macrophage-intrinsic inducer and dominant effector of this program: recombinant MMP12 was sufficient to enforce fibrogenic differentiation, clustering, and epithelioid features in mouse and human macrophages, whereas selec
Using a myeloid-specific Tsc2 deletion mouse model that recapitulates key features of human cardiac sarcoidosis, and integrating single-cell RNA sequencing, bioinformatic analyses, histopathology, in vitro functional studies in murine and human macrophages, and in vivo pharmacologic inhibition, the study found that constitutive mTORC1 activation induces a fibrogenic macrophage (FibMac) population in the heart characterized by Cd63, Spp1, Gpnmb, and Fabp5 expression and arising through a TGF-β-dependent monocyte-to-FibMac differentiation process, and identified MMP12 as a macrophage-intrinsic inducer and dominant effector of this program: recombinant MMP12 was sufficient to enforce fibrogenic differentiation, clustering, and epithelioid features in mouse and human macrophages, whereas selec
Using a myeloid-specific Tsc2 deletion mouse model that recapitulates key features of human cardiac sarcoidosis, and integrating single-cell RNA sequencing, bioinformatic analyses, histopathology, in vitro functional studies in murine and human macrophages, and in vivo pharmacologic inhibition, the study found that constitutive mTORC1 activation induces a fibrogenic macrophage (FibMac) population in the heart characterized by Cd63, Spp1, Gpnmb, and Fabp5 expression and arising through a TGF-β-dependent monocyte-to-FibMac differentiation process, and identified MMP12 as a macrophage-intrinsic inducer and dominant effector of this program: recombinant MMP12 was sufficient to enforce fibrogenic differentiation, clustering, and epithelioid features in mouse and human macrophages, whereas selec
发表出处待核验 This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.
This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.
This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.
This commentary, responding to Stein et al.'s critical review of big data in psychiatry, argues that psychiatry has swung from 'all theory and no data' to 'all data and no theory,' and proposes three directions: greater consideration of theory in big data use, development of idiographic psychiatry, and integration of generative AI into psychiatric research and practice.
DOAJ (DOAJ: Directory of Open Access Journals) Taking splicing methods in leather bag design as its object, the study first used Saussurean semiotics to analyze the symbiotic relationship between "signifiers" such as material, technique and element splicing and their corresponding "signifieds" of function, aesthetics and connotation, then determined target demand and style positioning through market research, constructed signifier-signified relationships with brainstorming and structured them into AIGC prompts to batch-generate 20 series of leather bag schemes, and finally ranked and selected the best through a CSAT satisfaction survey; results show that the individual alpha values of the 20 bags all exceed 0.80, KMO=0.8664 with a significant Bartlett sphericity test (P<0.001), average overall satisfaction ranges from 3.34 to 3.
Taking splicing methods in leather bag design as its object, the study first used Saussurean semiotics to analyze the symbiotic relationship between "signifiers" such as material, technique and element splicing and their corresponding "signifieds" of function, aesthetics and connotation, then determined target demand and style positioning through market research, constructed signifier-signified relationships with brainstorming and structured them into AIGC prompts to batch-generate 20 series of leather bag schemes, and finally ranked and selected the best through a CSAT satisfaction survey; results show that the individual alpha values of the 20 bags all exceed 0.80, KMO=0.8664 with a significant Bartlett sphericity test (P<0.001), average overall satisfaction ranges from 3.34 to 3.
Taking splicing methods in leather bag design as its object, the study first used Saussurean semiotics to analyze the symbiotic relationship between "signifiers" such as material, technique and element splicing and their corresponding "signifieds" of function, aesthetics and connotation, then determined target demand and style positioning through market research, constructed signifier-signified relationships with brainstorming and structured them into AIGC prompts to batch-generate 20 series of leather bag schemes, and finally ranked and selected the best through a CSAT satisfaction survey; results show that the individual alpha values of the 20 bags all exceed 0.80, KMO=0.8664 with a significant Bartlett sphericity test (P<0.001), average overall satisfaction ranges from 3.34 to 3.
Taking splicing methods in leather bag design as its object, the study first used Saussurean semiotics to analyze the symbiotic relationship between "signifiers" such as material, technique and element splicing and their corresponding "signifieds" of function, aesthetics and connotation, then determined target demand and style positioning through market research, constructed signifier-signified relationships with brainstorming and structured them into AIGC prompts to batch-generate 20 series of leather bag schemes, and finally ranked and selected the best through a CSAT satisfaction survey; results show that the individual alpha values of the 20 bags all exceed 0.80, KMO=0.8664 with a significant Bartlett sphericity test (P<0.001), average overall satisfaction ranges from 3.34 to 3.
发表出处待核验 This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.
This review spans seven areas—community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials—to assess the advances, constraints and future directions of big data in psychiatry, concluding that big data has fostered transdisciplinarity and illuminated the intricacy, heterogeneity and variability of mechanisms underlying psychiatric disorders, yet sample size alone does not guarantee more precise estimates and a gap remains between big data and clinical application, so future work must attend equally to data quality and methodological rigor, conceptual models, and clinical questions.