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    University Hospital North Norway Reports Findings in Prostatectomy(Robotic assi sted simple prostatectomy mitigates perioperativemorbidity compared to open sim ple prostatectomy - a singleinstitution report)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Surgery - Prostatectom y is the subject of a report. According tonews reporting originating from Troms o, Norway, by NewsRx correspondents, research stated, “Accordingto the guidelin es of the European Association of Urology, open simple prostatectomy should be o fferedto men with a prostate size exceeding 80 mL suffering from moderate to se vere LUTS in the absence of atransurethral enucleation technique. However, open simple prostatectomy is associated with complicationssuch as bleeding, blood t ransfusions and increased length of stay compared to minimally invasive procedures.”Our news editors obtained a quote from the research from University Hospital Nor th Norway, “The aimof the study was to compare perioperative data from the firs t cases of robotic assisted simple prostatectomy(RASP) to that of patients subj ected to open simple prostatectomy (OSP) at our department. Thepatients were id entified by a search for the respective procedure codes. In the OSP group enucle ation ofthe adenoma was performed through the prostatic capsule (Millin procedu re), while access to the adenomawas gained through the bladder in the RASP grou p. Complications were scored according to the Clavien-Dindo classification syste m. 27 patients who underwent OSP were retrospectively identified and comparedto the first 26 patients who were subjected to RASP. The groups were similar with respect to age, bodymass index and ASA score. Operative time was significantly shorter in the OSP group compared to theRASP group. Bleeding volume, drop in po stoperative hemoglobin and the number of blood transfusionswere all significant ly higher in the OSP group compared to the RASP group. Average length of stay was 5.5 (2-18) days in the OSP group compared to 1.6 (1-5) days in the RASP group (p <0.001). Thenumber of postoperative complications, Cla vien-Dindo 2, were significantly higher in the OSP group (11)compared to the RA SP group (none, p<0.001).”
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    Anhui Jianzhu University Reports Findings in Robotics (Lightsteerablelocomotio n using zero-elastic-energy modes)

    22-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Robotics is the subjec t of a report. According to news reportingoriginating in Hefei, People’s Republ ic of China, by NewsRx journalists, research stated, “Driving syntheticmaterial s out of equilibrium via dissipative mechanisms paves the way towards autonomous , self-sustainedrobotic motions. However, obtaining agile movement in diverse e nvironments with dynamic steerabilityremains a challenge.”The news reporters obtained a quote from the research from Anhui Jianzhu Univers ity, “Here wereport a light-fuelled soft liquid crystal elastomer torus with se lf-sustained out-of-equilibrium movement.Under constant light excitation, the t orus undergoes spontaneous rotation arising from the formation ofzero-elastic-e nergy modes. By exploiting dynamic friction or drag, the zero-elastic-energy-mod e-basedlocomotion direction can be optically controlled in various dry and flui d environments. We demonstrate theability of the liquid crystal elastomer torus to laterally and vertically swim in the Stokes regime. The torusnavigation can be extended to three-dimensional space with full steerability of the swimming d irection.”
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    Tongji University School of Medicine Reports Findings in PersonalizedMedicine ( Integrated approach of machine learning, Mendelianrandomization and experimenta l validation for biomarker discoveryin diabetic nephropathy)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Drugs and Therapies - Personalized Medicine is the subject of areport. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents,research st ated, “To identify potential biomarkers and explore the mechanisms underlying di abeticnephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimentalvalidation. Microarray and RNA-sequencing datasets (GSE47 184, GSE96804, GSE104948, GSE104954,GSE142025 and GSE175759) were obtained from the Gene Expression Omnibus database.”Financial support for this research came from Shanghai Municipal Health Commissi on.Our news journalists obtained a quote from the research from the Tongji Universi ty School of Medicine,“Differential expression analysis identified the differen tially expressed genes (DEGs) between patients withDN and controls. Diverse mac hine learning algorithms, including least absolute shrinkage and selectionopera tor, support vector machine-recursive feature elimination, and random forest, we re used to enhancegene selection accuracy and predictive power. We integrated s ummary-level data from genome-wideassociation studies on DN with expression qua ntitative trait loci data to identify genes with potentialcausal relationships to DN. The predictive performance of the biomarker gene was validated using rece iveroperating characteristic (ROC) curves. Gene set enrichment and correlation analyses were conducted toinvestigate potential mechanisms. Finally, the biomar ker gene was validated using quantitative real-timepolymerase chain reaction in clinical samples from patients with DN and controls. Based on identified 314DE Gs, seven characteristic genes with high predictive performance were identified using three integratedmachine learning algorithms. MR analysis revealed 219 gen es with significant causal effects on DN,ultimately identifying one co-expresse d gene, carbonic anhydrase II (CA2), as a key biomarker for DN.The ROC curves d emonstrated the excellent predictive performance of CA2, with area under the curve values consistently above 0.878 across all datasets. Additionally, our analys is indicated a significantassociation between CA2 and infiltrating immune cells in DN, providing potential mechanistic insights.This biomarker was validated u sing clinical samples, confirming the reliability of our findings in clinical practice. By integrating machine learning, MR and experimental validation, we succ essfully identified andvalidated CA2 as a promising biomarker for DN with excel lent predictive performance. The biomarkermay play a role in the pathogenesis a nd progression of DN via immune-related pathways.”
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    University of Notre Dame Reports Findings in Artificial Intelligence(An Artific ial Intelligence Algorithm for Detection of Severe AorticStenosis: A Clinical C ohort Study)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – New research on Artificial Intelligence is the su bject of a report. According to news reportingoriginating in Fremantle, Austral ia, by NewsRx journalists, research stated, “Identifying individuals withsevere aortic stenosis (AS) at high risk of mortality remains challenging using curren t clinical imagingmethods. The purpose of this study was to evaluate an artific ial intelligence decision support algorithm(AI-DSA) to augment the detection of severe AS within a well-resourced health care setting.”The news reporters obtained a quote from the research from the University of Not re Dame, “Agnostic toclinical information, an AI-DSA trained to identify echoca rdiographic phenotype associated with an aorticvalve area (AVA) <1 cm using minimal input data (excluding left ventricular outflow tract measures ) wasapplied to routine transthoracic echocardiograms (TTE) reports from 31,141 U.S. Medicare beneficiariesat an academic medical center (2003-2017). Performa nce of AI-DSA to detect the phenotype associatedwith an AVA <1 cm was excellent (sensitivity 82.2%, specificity 98.1% , negative predictive value 9.2%,c-statistic = 0.986). In addition to identifying clinical severe AS cases, AI-DSA identified an additional1,034 (3.3%) individuals with guideline-defined moderate AS but with a si milar clinical and TTE phenotypeto those with severe AS with low rates of aorti c valve replacement (6.6%). Five-year mortality was 75.9% in those with known severe AS, 73.5% in those with a similar pheno type to severe AS, and 44.6% inthose without severe AS. The AI-DS A continued to perform well to identify severe AS among those with adepressed l eft ventricular ejection fraction. Overall rates of aortic valve replacement rem ained low, evenin those with an AVA <1 cm (21.9% ). Without relying on left ventricular outflow tract measurements,an AI-DSA use d echocardiographic reports to reliably identify the phenotype of severe AS.”
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    Findings from China University of Geosciences Wuhan Provides NewData about Mach ine Learning (Machine Learning for SubsurfaceGeological Feature Identification From Seismic Data: Methods,Datasets, Challenges, and Opportunities)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting from Hubei, People’s Republ ic of China, by NewsRx journalists, research stated, “Identificationof geologic al features from seismic data such as faults, salt bodies, and channels, is esse ntial for studiesof the shallow Earth, natural disaster forecasting and evaluat ion, carbon capture and storage, hydrogenstorage, geothermal energy development , and traditional resource exploration. However, manual seismicinterpretation i s distinctly subjective and labor-intensive.”Financial supporters for this research include China Scholarship Council, Key Re search and DevelopmentProject of Hubei Province Technology Innovation Plan, Nat ional Key Research & Development Programof China.
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    University of Sydney Reports Findings in Artificial Intelligence (RadiomicAnaly sis of Cohort-Specific Diagnostic Errors in ReadingDense Mammograms Using Artif icial Intelligence)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reportingoriginating in Sydney, Australia, by NewsRx journalists, research stated, “This study aims to investigate radiologists’ interpretation errors when reading dense screening mammograms using a radiomics-basedartificial intelligence approach. Thirty-six radiologist s from China and Australia read 60 dense mammograms.”The news reporters obtained a quote from the research from the University of Syd ney, “For eachcohort, we identified normal areas that looked suspicious of canc er and the malignant areas containingcancers. Then radiomic features were extra cted from these identified areas and random forest models weretrained to recogn ize the areas that were most frequently linked to diagnostic errors within each cohort.The performance of the model and discriminatory power of significant rad iomic features were assessed. Wefound that in the Chinese cohort, the AUC value s for predicting false positives were 0.864 (CC) and 0.829(MLO), while in the A ustralian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, theAUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Au stralian cohort, theywere 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum responsefilter outputs were more prone to false p ositives, while areas with significant intensity changes and coarsetextures wer e more likely to yield false negatives. This cohort-based pipeline proves effect ive in identifyingcommon errors for specific reader cohorts based on image-deri ved radiomic features.”
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    Researchers from Guangzhou University Detail Findings in MachineLearning (A Stu dy On Global Oceanic Chlorophyll-a ConcentrationInversion Model for Modis Using Machine Learning Algorithms)

    27-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Fresh data on Machine Learning are presented in a new report. According to news reportingoriginating in Guangzhou, People’s Repu blic of China, by NewsRx journalists, research stated, “Machinelearning (ML) al gorithms can accurately extract quantitative patterns from datasets without requ iring priorknowledge, playing an increasingly crucial role in tasks such as inf ormation extraction from remotely senseddata. This study employs several ML alg orithms, including Back Propagation Network (BPN), SupportVector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to developglobal oceanic chlorophyll-a (Chl-a) concentration inversion models for MODIS/Aqua (MOD ISA).”Funders for this research include National Key Research & Developm ent Program of China, NationalNatural Science Foundation of China (NSFC), Basic and Applied Basic Research Project of Guangzhou,Special Projects in Key Fields of Universities in Guangdong Province.
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    Reports from Xi’an Jiaotong Liverpool University Provide New Insightsinto Machi ne Learning (Stress-strain Behaviour of AxiallyLoaded Frp-confined Natural and Recycled Aggregate Concrete UsingDesign-oriented and Machine Learning Approache s)

    28-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According to newsreporting out of Jiangsu, People’s Re public of China, by NewsRx editors, research stated, “The reuse ofrecycled aggr egates (RA) in fresh concrete helps to address construction waste management and reducescarbon footprints; however, the resulting recycled aggregate concrete ( RAC) possesses some undesirableproperties for structural applications. While st rengthening RAC with fibre-reinforced polymers (FRP) helpsto mitigate these iss ues, predicting the stress-strain behaviours of FRP-confined RAC (FRCRAC) and natural aggregate concrete (FRCNAC) can be complex.”Financial supporters for this research include Xi’an Jiaotong-Liverpool Universi ty, Nigerian PetroleumTechnological Development Fund (PTDF) at Xi’an Jiaotong-L iverpool University.
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    Reports from University of Lincoln Advance Knowledge in MachineLearning (Interp retable Spatial Machine Learning Insights Into UrbanSanitation Challenges: a Ca se Study of Human Feces DistributionIn San Francisco)

    29-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Current study results on Machine Learn ing have been published. According tonews reporting originating in Lincoln, Uni ted Kingdom, by NewsRx journalists, research stated, “Urbansanitation is critic al for public health, with the management of human feces presenting significant challengesin growing urban areas. While prior research has concentrated on the health impacts of fecal contaminants,the spatial distribution and determinants of open defecation in urban contexts have received less attention.”Financial support for this research came from National Science Foundation (NSF).The news reporters obtained a quote from the research from the University of Lin coln, “To address thesegaps, this study proposed an interpretable spatial machi ne learning framework integrating GeographicallyWeighted Random Forest (GW-RF) and SHapley Additive exPlanations (SHAP) analysis to reveal thecomplex spatial heterogeneity and factors influencing feces density in cities, taking San Franci sco asa case study. Our findings highlight that homelessness, population densit y, and building density arecritical drivers of feces distribution. Importantly, higher restroom density was linked to increased fecesdensity, underscoring the need for urban planning to focus on improving restroom accessibility rather tha nmerely increasing their number. Additionally, our research suggests that green spaces serve as a mitigatingfactor, indicating that enhancing urban greenery c ould be an effective strategy for addressing sanitationchallenges.”
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    University of Catania Reports Findings in Artificial Intelligence(Non-invasive physiological assessment of intermediate coronarystenoses from plain angiograph y through artificial intelligence: theSTARFLOW system)

    30-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Artificial Intelligenc e is the subject of a report. According tonews reporting originating in Catania , Italy, by NewsRx journalists, research stated, “Despite evidencesupporting us e of fractional flow reserve (FFR) and instantaneous waves-free ratio (iFR) to i mproveoutcome of patients undergoing coronary angiography (CA) and percutaneous coronary intervention, suchtechniques are still underused in clinical practice due to economic and logistic issues. We aimed to developan artificial intellig ence (AI)-based application to compute FFR and iFR from plain CA.”The news reporters obtained a quote from the research from the University of Cat ania, “Consecutivepatients performing FFR or iFR or both were enrolled. A speci fic multi-task deep network exploiting 2projections of the coronary of interest from standard CA was appraised. Accuracy of prediction of FFR/iFRof the AI mod el was the primary endpoint, along with sensitivity and specificity. Prediction was testedboth for continuous values and for dichotomous classification (positi ve/negative) for FFR or iFR. Subgroupanalyses were performed for FFR and iFR.A total of 389 patients from 5 centers were enrolled. Mean agewas 67.9 ? 9.6 and 39.2% of patients were admitted for acute coronary syndrome. Overa ll, the accuracywas 87.3% (81.2-93.4%), with a sensi tivity of 82.4% (71.9-96.4%) and a specificity of 92. 2% (90.4-93.9%). For FFR, accuracy was 84.8% (77.8-91.8%), with a sensitivity of 81.9% (69.4-94.4% ) and aspecificity of 87.7% (85.5-89.9%), while for iFR accuracy was 90.2% (86.0-94.6%), with a sensitivi ty of87.2% (76.6-97.8%) and a specificity of 93.2% (91.7-94.7%, all confidence intervals 95%).”
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