简介：AbstractMachine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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简介：Objective:Toinvestigatethefeasibilityoftranscanalendoscopicmyringoplastyinthehandofyoungbeginnersurgeonswhohadjustcompletedtheresidencyprogramme.Methods:Inathreeyearperiod(August2012toAugust2015),44earsin42patientswereoperateduponbyabeginnersurgeonthroughthetranscanalendoscopicapproachinasubdistrictlevelhospitallocatedinthenorthwesternrangesoftheHimalayanregion.Results:Ofthe42patient,19weremaleand23female.Themeanagewas26.23years(range:15e47years).In40ears,completeperforationclosurewasachievedatsixmonths(successrate:90.9%).ThemeanairconductionPTApreoperativelywas40.84dBHLandimprovedto28.06dBHLpostoperatively(p<.001).ThemeanABgappreoperativelywas22.40dB,whichimprovedto9.1dBpostoperatively(p<.001).Conclusion:Endoscopictranscanalmyringoplastyissafeandreliableeveninyoungbeginners'hands.Surgeonscanconsiderendoscopicapproachearlyintheircareerswithoutthefearoflearningcurve.Thecostofendoscopicequipmentisaboutonetenthascomparedtoopenapproachunderaoperatingmicroscope,andanaddedadvantage.

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简介：AbstractPurpose:Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.Methods:A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.Results:There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.Conclusion:The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

简介：AbstractIn the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule-based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

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简介：摘要：传统上，国内高等教育课程文件是依靠在院校教学大纲完成的，即将踏入临床护生所学内容与临床活动内容往往存在巨大差异。随着护理学科的发展，同时，对护理人员也提出了更高的要求，实习护士如何做好自身准备也成为我们一直探讨的问题。传统背景下，护生该如何找到突破口，提高实习质量，护生面临更大挑战，菲律宾圣托马斯大学理学硕士提出高等教育的质量推动转向OBL学习方法，即护生带着“wish list{1}进入临床活动，根据自身能力制定学习计划，是护生主动将所学的理论知识与实践相结合并巩固加深的重要环节，使护生在学校学习的理论知识应用于临床实践，培养和提高临床思维分析和独立自主解决问题的能力。

简介：AbstractBackground:Minimally invasive pancreatic surgery (MIPS) has developed over the last 3 decades and is nowadays experiencing an increased interest from the surgical community. With increasing awareness of both the public and the surgical community on patient safety, optimization of training has gained importance. For implementation of MIPS we propose 3 training phases. The first phase focuses on developing basic skills and procedure specific skills with the help of simulation, biotissue drills, video libraries, live case observations, and training courses. The second phase consists of index procedures, fellowships, and proctoring programs to ensure patient safety during the first procedures. During the third phase the surgeons aim is to safely implement the procedure into standard practice while minimizing learning curve related excess morbidity and mortality. Case selection, skills assessment, feedback, and mentoring are important methods to optimize this phase. The residual learning curve can reach up to 100 cases depending on the surgeons’ previous experience, selection of cases, and definition of the parameters used to assess the learning curve. Adequate training and high procedural volume are key to implementing MIPS safely.

简介：BACKGROUND:Studieshaveshownthatneurogenesisinthedentategyrusplaysanimportantroleinlearningandmemory.However,studieshavenotdeterminedwhetherthesuperiorcervicalganglionorthesympatheticnervesysteminfluenceshippocampalneurogenesisorlearningandmemoryinadultrats.OBJECTIVE:Toobservedifferencesindentategyrusneurogenesis,aswellaslearningandmemory,inadultratsfollowingsuperiorcervicalganglionectomy.DESIGN,TIMEANDSETTING:Arandomized,controlled,animalstudywasperformedattheImmunohistochemistryLaboratoryoftheSchoolofLifeSciencesinLanzhouUniversityfromJuly2006toJuly2007.MATERIALS:DoublecortinpolyclonalantibodywasprovidedbySantaCruzBiotechnology,USA;avidin-biotin-peroxidasecomplexwaspurchasedfromZhongshanGoldenbrideBiotechnology,China;MorriswatermazewasboughtfromTaimengTechnology,China.METHODS:Atotalof20adult,male,Wistarratswererandomlydividedintosurgeryandcontrolgroups,with10ratsineachgroup.Inthesurgerygroup,thebilateralsuperiorcervicalganglionsweretransected.Inthecontrolgroup,thesuperiorcervicalganglionswereonlyexposed,butnoganglionectomywasperformed.MAINOUTCOMEMEASURES:Toexaminedistribution,morphology,andnumberofnewbornneuronsinthedentategyrususingdoublecortinimmunohistochemistryat36daysfollowingsurgicalprocedures.ToexamineabilityoflearningandmemoryinadultratsusingtheMorriswatermazeat30daysfollowingsurgicalprocedures.RESULTS:Doublecortinimmunohistochemicalresultsshowedthatareductioninthenumberofdoublecortin-positiveneuronsinthesurgerygroupcomparedtothecontrolgroup(P<0.05),whilethedistributionofdoublecortin-positiveneuronswasidenticalinthetwogroups.Thesurgerygroupexhibitedsignificantlyworseperformanceinlearningandspatialmemorytaskscomparedtothecontrolgroup(P<0.05).CONCLUSION:Superiorcervicalganglionectomyinhibitedneurogenesisinthedentategyrusanddecreasedlearningandmemorya

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简介：AbstractBackground:A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.Methods:We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.Results:We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.Conclusions:In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.

简介：AbstractBackground:Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animalorigin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.Methods:A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models.Results:The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor.Conclusions:Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

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简介：AbstractBackground:Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence.Methods:A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals.Results:We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements.Conclusions:Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.

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简介：院外静脉溶栓治疗AMI方法 ,静脉溶栓治疗AMI简单,一院外诊断AMI需要快速、准确