Nonlinearity vs nonlocality with emphasis on bandwidth broadening in semiconductor-based 1d metamaterials.

The physics of nonlinear optical materials is incredibly versatile, with the design of novel materials and structures offering numerous degrees of freedom. Nevertheless, weak inherent nonlinearity of conventional optical materials continues to hinder the progress of a number of important applications. In this study, we delve into the realm of broadband enhancement of nonlinearity within one-dimensional (1d) plasmonic metamaterials, exploring its intricate connection with nonlocality. Specifically, we introduce a phenomenological framework for quantifying the effective third-order nonlinear susceptibility of 1d multiphase plasmonic nanostructures, utilizing heavily doped semiconductors, and subsequently applying this approach using realistic material parameters. Both direct and inverse problems of nonlinearity enhancement have been addressed. Our findings demonstrate a remarkable capability to significantly augment the third-order nonlinear susceptibility across a defined frequency range, while concurrently gauging the impact of nonlocality on this enhancement.

Computational Screening of a Single-Atom Catalyst Supported by Monolayer Nb2S2C for Oxygen Reduction Reaction.

The search for high-performance catalysts to improve the catalytic activity for an oxygen reduction reaction (ORR) is crucial for developing a proton exchange membrane fuel cell. Using the first-principles method, we have performed computational screening on a series of transition metal (TM) atoms embedded in monolayer Nb2S2C to enhance the ORR activity. Through the scaling relationship and volcano plot, our results reveal that the introduction of a single Ni or Rh atom through substitutional doping into monolayer Nb2S2C yields promising ORR catalysts with low overpotentials of 0.52 and 0.42 V, respectively. These doped atoms remain intact on the monolayer Nb2S2C even at elevated temperatures. Importantly, the catalytic activity of the Nb2S2C doped with a TM atom can be effectively correlated with an intrinsic descriptor, which can be computed based on the number of d orbital electrons and the electronegativity of TM and O atoms.

Survival Analysis as a Basis for Testing Hypotheses when Using Quantitative Ordinal Scale Disease Severity Data.

Disease severity in plant pathology is often measured by the amount of a plant or plant part that exhibits disease symptoms. This is typically assessed using a numerical scale, which allows a standardized, convenient, and quick method of rating. These scales, known as quantitative ordinal scales (QOS), divide the percentage scale into a predetermined number of intervals. There are various ways to analyze these ordinal data, with traditional methods involving the use of midpoint conversion to represent the interval. However, this may not be precise enough, as it is only an estimate of the true value. In this case, the data may be considered interval-censored, meaning that we have some knowledge of the value but not an exact measurement. This type of uncertainty is known as censoring, and techniques that address censoring, such as survival analysis (SA), use all available information and account for this uncertainty. To investigate the pros and cons of using SA with QOS measurements, we conducted a simulation based on three pathosystems. The results showed that SA almost always outperformed midpoint conversion with data analyzed using a t test, particularly when data were not normally distributed. Midpoint conversion is currently a standard procedure. In certain cases, the midpoint approach required a 400% increase in sample size to achieve the same power as the SA method. However, as the mean severity increases, fewer additional samples are needed (approximately an additional 100%), regardless of the assessment method used. Based on these findings, we conclude that SA is a valuable method for enhancing the power of hypothesis testing when analyzing QOS severity data. Future research should investigate the wider use of survival analysis techniques in plant pathology and their potential applications in the discipline.

Impact of polygenic risk score for triglyceride trajectory and diabetic complications in subjects with type 2 diabetes based on large electronic medical record data from Taiwan: a case control study.

BACKGROUND: The prevalence of diabetic dyslipidemia has gradually increased worldwide and individuals with hypertriglyceridemia often have a high polygenic burden of triglyceride (TG)-increasing variants. However, the contribution of genetic variants to dyslipidemia in patients with type 2 diabetes (T2D) remains limited. Therefore, in this study, we aimed to investigate the genetic characteristics of longitudinal changes in TG levels among patients with T2D and summarize the genetic effects of polygenic risk score (PRS) on TG trajectory and risk of diabetic complications. METHODS: We conducted a case-control study. A total of 11,312 patients with T2D with longitudinal TG and genetic data were identified from a large hospital database in Taiwan. We then performed a genome-wide association study and calculated the relative PRS. RESULTS: In total, 21 single-nucleotide polymorphisms (SNPs) related to TG trajectory were identified and yielded an area under the receiver operating characteristic curve (ROC) of 0.712 for high TG trajectory risk among Taiwanese patients with T2D. A cumulative genetic effect was observed for high TG trajectory, even when considering the adherence of a lipid-lowering agent in stratified analysis. An increased PRS increases high TG trajectory risk in a logistic regression model (odds ratio = 1.55; 95% confidence interval [CI] = 1.31-1.83 in the validation cohort). The TG-specific PRS was associated with the risk of diabetic microvascular complications, including diabetic retinopathy and nephropathy (with hazard ratios of 1.11 [95% CI = 1.01-1.21, P = 0.027] and 1.05 [95% CI = 1.01-1.1, P = 0.018], respectively). CONCLUSIONS: This study may contribute to the identification of patients with T2D who are at risk of abnormal TG levels and diabetic microvascular complications using polygenic information.

Nightshift work and irregular menstrual cycle: 8-year follow-up cohort study.

BACKGROUND: Irregular menstruation is a major health problem among women, although its association with nightshift work remains controversial. AIMS: To study the association between nightshift work and irregular menstrual cycle among female workers and investigate any differences according to sleep quality, working hours or obesity. METHODS: This study included female workers who underwent health examinations from 2012 to 2019. Nightshift work, working hours, sleep quality and menstrual cycles were assessed using self-administered questionnaires. Irregular menstrual cycle was defined as self-reported irregular or ≥36 days. Adjusted odds ratios and 95% confidence intervals (CIs) were calculated by multivariable logistic regression; adjusted hazard ratios (95% CIs) for incident irregular menstrual cycle were calculated by Cox proportional hazard models with time-dependent analysis. RESULTS: The study participants were 87 147 in the cross-sectional study and 41 516 in the longitudinal study. After adjusting for all covariates in the cross-sectional analyses, the odds ratio for prevalent irregular menstrual cycle among female nightshift workers versus the reference was 1.26 (95% CI 1.2-1.33). In the cohort study, the adjusted hazard ratio for incident irregular menstrual cycle among nightshift workers was 1.95 (95% CI 1.61-2.35) in the period after 6 years. No significant differences were observed among subgroups stratified by sleep quality, working hours or obesity. CONCLUSIONS: Nightshift work is associated with an increased risk of both prevalent and incident irregular menstrual cycle in female workers without significant interactions by sleep quality, working hours or obesity.

[Effect of sirolimus combined with anti-CD20 monoclonal antibody desensitization on the prognosis of patients underwent haploidentical stem cell transplantation].

Objective: To investigate the effects of sirolimus combined with anti-CD20 monoclonal antibody desensitization on the prognosis of patients with haploidentical stem cell transplantation (haplo-SCT). Methods: Fifteen consecutive patients who received haplo-SCT and pre-transplant donor specific anti-human leukocyte antigen (HLA) antibody (DSA) positive [mean fluorescence intensity (MFI)≥2 000] in the Institute of Hematological Diseases from November 2021 to March 2023 were retrospectively recruited into the desensitized group. There were 4 males and 11 females, with a median age [M(Q1, Q3)] of 48 (37, 59) years. All patients were desensitized with sirolimus combined with anti-CD20 monoclonal antibody. The non-desensitized group included 29 patients with haplo-SCT who had not received desensitization treatment from August 2012 to June 2016. There were 12 males and 17 females with a median age of 42 (26, 50) years. Up to October 1, 2023, the median follow-up time was 13 (9, 18) months in the study group and 23 (14, 29) months in the control group. The changes of MFI before and after desensitization treatment and the prognosis of patients in the desensitized group were compared, including the incidence of primary implantation failure (pGF), neutrophil implantation time, platelet implantation time, grade Ⅱ-Ⅳ acute graft-versus-host disease (GVHD) and chronic GVHD incidence, non-recurrence related mortality, event-free survival rate, disease-free survival rate and overall survival rate. The survival curve was drawn by Kaplan-Meier method, and the survival rate between groups was compared with Log-rank test. Results: After desensitization treatment, the level of DSA MFI in the desensitized group decreased from 8 879 (7 544, 11 495) to 3 781 (1 638, 4 165) after desensitization treatment (P<0.01). All of the patients achieved hematopoietic recovery, and the median time for neutrophil and platelet engraftment were 14 (11, 15) and 20 (18, 25) days, respectively. The incidence of pGF in the desensitized group was 0, which was lower than that in the non-desensitized group (34.5%, 10/29) (P=0.011). The expected 1-year disease-free survival rate and overall survival rate in the desensitized group were 100% (15/15) and 100% (15/15) respectively, while those in the non-desensitized group were 75.9% (22/29) and 75.9% (22/29) respectively, the difference was not statistically significant (both P>0.05). The one-year event-free survival rate in the desensitized group was expected to be 100% (15/15), which was higher than that in the non-desensitized group (51.3%, 15/29) (P=0.002). Conclusion: Sirolimus combined with anti-CD20 monoclonal antibody desensitization therapy can reduce the DSA level of haplo-SCT recipients, promote hematopoietic engraftment after transplantation, and avoid the occurrence of pGF after transplantation.

[Early to mid-term clinical outcomes of aortic valve repair in patients with bicuspid aortic insufficiency].

Objective: To examine the early to mid-term clinical outcomes of aortic valve repair in patients with bicuspid aortic insufficiency. Methods: This is a retrospective cohort study. The clinical data of 124 patients with BAV insufficiency who underwent aortic valve repair from January 2017 to June 2023 in the Department of Cardiovascular Surgery at Fuwai Hospital were analyzed retrospectively. There were 117 males and 7 females with an age of (38.1±12.7) years (range: 14 to 65 years). Depending on whether the aortic sinus was replaced or not, surgical approaches were divided into valve sparing root replacement (reimplantation, remodeling, modified remodeling) and isolated aortic valve repair (annuloplasty, isolated aortic valve leaflet repair). Perioperative and follow-up data were collected. Kaplan-Meier method was used to plot the curves of survival rate, free recurrence rate of massive aortic valve insufficiency and free re-operation rate, and Log-rank test was used for comparison between groups. Results: Among the surgeries, there were 47 cases of reimplantation, 8 cases of remodeling, 8 cases of modified remodeling, 48 cases of aortic annuloplasty (external annuloplasty in 22 cases, CV-0 annuloplasty in 26 cases), and 13 cases of isolated leaflet repair. Leaflet plication was the most used leaflet repair technique, used in 103 patients. The cardiopulmonary bypass time was (133.7±56.9) minutes (range: 48 to 461 minutes), and aortic cross-clamp time was (103.8±47.8) minutes (range: 25 to 306 minutes), with no surgical mortality. All patients underwent outpatient or telephone follow-up. The cumulative follow-up time was 340.3 person-years and the mean follow-up time was (M (IQR)) 34.0 (25.5) months (range: 3 to 76 months). The 5-year survival rate was 98.4%, the 5-year freedom from significant insufficiency rate was 93.4% and the 5-year freedom from aortic valve reoperation rate was 95.6%. The subgroup analysis revealed a significantly better freedom from the significant insufficiency rate in the aortic valve annular reduction group compared to the non-reduction group (P<0.01). Conclusions: Aortic valve repair in patients with bicuspid aortic insufficiency could obtain steady early to mid-term outcomes. Aortic annuloplasty can reduce the risk of recurrent aortic valve insufficiency in patients undergoing aortic repair.

Improved Measurement of the Evolution of the Reactor Antineutrino Flux and Spectrum at Daya Bay.

Reactor neutrino experiments play a crucial role in advancing our knowledge of neutrinos. In this Letter, the evolution of the flux and spectrum as a function of the reactor isotopic content is reported in terms of the inverse-beta-decay yield at Daya Bay with 1958 days of data and improved systematic uncertainties. These measurements are compared with two signature model predictions: the Huber-Mueller model based on the conversion method and the SM2018 model based on the summation method. The measured average flux and spectrum, as well as the flux evolution with the ^{239}Pu isotopic fraction, are inconsistent with the predictions of the Huber-Mueller model. In contrast, the SM2018 model is shown to agree with the average flux and its evolution but fails to describe the energy spectrum. Altering the predicted inverse-beta-decay spectrum from ^{239}Pu fission does not improve the agreement with the measurement for either model. The models can be brought into better agreement with the measurements if either the predicted spectrum due to ^{235}U fission is changed or the predicted ^{235}U, ^{238}U, ^{239}Pu, and ^{241}Pu spectra are changed in equal measure.

Properties of Cosmic-Ray Sulfur and Determination of the Composition of Primary Cosmic-Ray Carbon, Neon, Magnesium, and Sulfur: Ten-Year Results from the Alpha Magnetic Spectrometer.

We report the properties of primary cosmic-ray sulfur (S) in the rigidity range 2.15 GV to 3.0 TV based on 0.38×10^{6} sulfur nuclei collected by the Alpha Magnetic Spectrometer experiment (AMS). We observed that above 90 GV the rigidity dependence of the S flux is identical to the rigidity dependence of Ne-Mg-Si fluxes, which is different from the rigidity dependence of the He-C-O-Fe fluxes. We found that, similar to N, Na, and Al cosmic rays, over the entire rigidity range, the traditional primary cosmic rays S, Ne, Mg, and C all have sizeable secondary components, and the S, Ne, and Mg fluxes are well described by the weighted sum of the primary silicon flux and the secondary fluorine flux, and the C flux is well described by the weighted sum of the primary oxygen flux and the secondary boron flux. The primary and secondary contributions of the traditional primary cosmic-ray fluxes of C, Ne, Mg, and S (even Z elements) are distinctly different from the primary and secondary contributions of the N, Na, and Al (odd Z elements) fluxes. The abundance ratio at the source for S/Si is 0.167±0.006, for Ne/Si is 0.833±0.025, for Mg/Si is 0.994±0.029, and for C/O is 0.836±0.025. These values are determined independent of cosmic-ray propagation.

Temporal Structures in Positron Spectra and Charge-Sign Effects in Galactic Cosmic Rays.

We present the precision measurements of 11 years of daily cosmic positron fluxes in the rigidity range from 1.00 to 41.9 GV based on 3.4×10^{6} positrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The positron fluxes show distinctly different time variations from the electron fluxes at short and long timescales. A hysteresis between the electron fluxes and the positron fluxes is observed with a significance greater than 5σ at rigidities below 8.5 GV. On the contrary, the positron fluxes and the proton fluxes show similar time variation. Remarkably, we found that positron fluxes are modulated more than proton fluxes with a significance greater than 5σ for rigidities below 7 GV. These continuous daily positron fluxes, together with AMS daily electron, proton, and helium fluxes over an 11-year solar cycle, provide unique input to the understanding of both the charge-sign and mass dependencies of cosmic rays in the heliosphere.

Temporal Structures in Electron Spectra and Charge Sign Effects in Galactic Cosmic Rays.

We present the precision measurements of 11 years of daily cosmic electron fluxes in the rigidity interval from 1.00 to 41.9 GV based on 2.0×10^{8} electrons collected with the Alpha Magnetic Spectrometer (AMS) aboard the International Space Station. The electron fluxes exhibit variations on multiple timescales. Recurrent electron flux variations with periods of 27 days, 13.5 days, and 9 days are observed. We find that the electron fluxes show distinctly different time variations from the proton fluxes. Remarkably, a hysteresis between the electron flux and the proton flux is observed with a significance of greater than 6σ at rigidities below 8.5 GV. Furthermore, significant structures in the electron-proton hysteresis are observed corresponding to sharp structures in both fluxes. This continuous daily electron data provide unique input to the understanding of the charge sign dependence of cosmic rays over an 11-year solar cycle.

Precision Measurement of Reactor Antineutrino Oscillation at Kilometer-Scale Baselines by Daya Bay.

We present a new determination of the smallest neutrino mixing angle θ_{13} and the mass-squared difference Δm_{32}^{2} using a final sample of 5.55×10^{6} inverse beta-decay (IBD) candidates with the final-state neutron captured on gadolinium. This sample is selected from the complete dataset obtained by the Daya Bay reactor neutrino experiment in 3158 days of operation. Compared to the previous Daya Bay results, selection of IBD candidates has been optimized, energy calibration refined, and treatment of backgrounds further improved. The resulting oscillation parameters are sin^{2}2θ_{13}=0.0851±0.0024, Δm_{32}^{2}=(2.466±0.060)×10^{-3} eV^{2} for the normal mass ordering or Δm_{32}^{2}=-(2.571±0.060)×10^{-3} eV^{2} for the inverted mass ordering.

First Measurement of the Nuclear-Recoil Ionization Yield in Silicon at 100 eV.

We measured the nuclear-recoil ionization yield in silicon with a cryogenic phonon-sensitive gram-scale detector. Neutrons from a monoenergetic beam scatter off of the silicon nuclei at angles corresponding to energy depositions from 4 keV down to 100 eV, the lowest energy probed so far. The results show no sign of an ionization production threshold above 100 eV. These results call for further investigation of the ionization yield theory and a comprehensive determination of the detector response function at energies below the keV scale.

Differentiation between normal and abnormal kidneys using 99mTc-DMSA SPECT with deep learning in paediatric patients.

AIM: To investigate the feasibility of using deep learning (DL) to differentiate normal from abnormal (or scarred) kidneys using technetium-99m dimercaptosuccinic acid (99mTc-DMSA) single-photon-emission computed tomography (SPECT) in paediatric patients. MATERIAL AND METHODS: Three hundred and one 99mTc-DMSA renal SPECT examinations were reviewed retrospectively. The 301 patients were split randomly into 261, 20, and 20 for training, validation, and testing data, respectively. The DL model was trained using three-dimensional (3D) SPECT images, two-dimensional (2D) maximum intensity projections (MIPs), and 2.5-dimensional (2.5D) MIPs (i.e., transverse, sagittal, and coronal views). Each DL model was trained to determine renal SPECT images into either normal or abnormal. Consensus reading results by two nuclear medicine physicians served as the reference standard. RESULTS: The DL model trained by 2.5D MIPs outperformed that trained by either 3D SPECT images or 2D MIPs. The accuracy, sensitivity, and specificity of the 2.5D model for the differentiation between normal and abnormal kidneys were 92.5%, 90% and 95%, respectively. CONCLUSION: The experimental results suggest that DL has the potential to differentiate normal from abnormal kidneys in children using 99mTc-DMSA SPECT imaging.

Quantitative assessment of cerebrospinal fluid flow and volume in enlargement of the subarachnoid spaces of infancy using MRI.

BACKGROUND: The etiology of enlarged subarachnoid spaces of infancy is unknown; however, there is radiologic similarity with normal pressure hydrocephalus. Adults with normal pressure hydrocephalus have been shown to have altered cerebrospinal (CSF) flow through the cerebral aqueduct. OBJECTIVE: To explore potential similarity between enlarged subarachnoid spaces of infancy and normal pressure hydrocephalus, we compared MRI-measured CSF flow through the cerebral aqueduct in infants with enlarged subarachnoid spaces of infancy to infants with normal brain MRIs. MATERIALS AND METHODS: This was an IRB approved retrospective study. Clinical brain MRI examinations including axial T2 imaging and phase contrast through the aqueduct were reviewed for infants with enlarged subarachnoid spaces of infancy and for infants with a qualitatively normal brain MRI. The brain and CSF volumes were segmented using a semi-automatic technique (Analyze 12.0) and CSF flow parameters were measured (cvi42, 5.14). All data was assessed for significant differences while controlling for age and sex using analysis of covariance (ANCOVA). RESULTS: Twenty-two patients with enlarged subarachnoid spaces (mean age 9.0 months, 19 males) and 15 patients with normal brain MRI (mean age 18.9 months, 8 females) were included. Volumes of the subarachnoid space (P < 0.001), lateral (P < 0.001), and third ventricles (P < 0.001) were significantly larger in infants with enlarged subarachnoid spaces of infancy. Aqueductal stroke volume significantly increased with age (P = 0.005), regardless of group. CONCLUSION: CSF volumes were significantly larger in infants with enlarged subarachnoid spaces of infancy versus infants with a normal MRI; however, there was no significant difference in CSF flow parameters between the two groups.

Multitrait meta-analyses identify potential candidate genes for growth-related traits in Holstein heifers.

Understanding the underlying pleiotropic relationships among growth and body size traits is important for refining breeding strategies in dairy cattle for optimal body size and growth rate. Therefore, we performed single-trait GWAS for monthly-recorded body weight (BW), hip height, body length, and chest girth from birth to 12 mo of age in Holstein animals, followed by stepwise multiple regression of independent or lowly-linked markers from GWAS loci using conditional and joint association analyses (COJO). Subsequently, we conducted a multitrait meta-analysis to detect pleiotropic markers. Based on the single-trait GWAS, we identified 170 significant SNPs, in which 59 of them remained significant after the COJO analyses. The most significant SNP, located at BTA7:3,676,741, explained 2.93% of the total phenotypic variance for BW6 (BW at 6 mo of age). We identified 17 SNPs with potential pleiotropic effects based on the multitrait meta-analyses, which resulted in 3 additional SNPs in comparison to those detected based on the single-trait GWAS. The identified quantitative trait loci regions overlap with genes known to influence human growth-related traits. According to positional and functional analyses, we proposed HMGA2, HNF4G, MED13L, BHLHE40, FRZB, DMP1, TRIB3, and GATAD2A as important candidate genes influencing the studied traits. The combination of single-trait GWAS and meta-analyses of GWAS results improved the efficiency of detecting associated SNPs, and provided new insights into the genetic mechanisms of growth and development in Holstein cattle.

Applications of machine learning in animal and veterinary public health surveillance.

Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their own performance at a given task (e.g. classification or prediction) based on data and without being explicitly and fully instructed on how to achieve this. Surveillance systems for animal and zoonotic diseases depend upon effective completion of a broad range of tasks, some of them amenable to ML algorithms. As in other fields, the use of ML in animal and veterinary public health surveillance has greatly expanded in recent years. Machine learning algorithms are being used to accomplish tasks that have become attainable only with the advent of large data sets, new methods for their analysis and increased computing capacity. Examples include the identification of an underlying structure in large volumes of data from an ongoing stream of abattoir condemnation records, the use of deep learning to identify lesions in digital images obtained during slaughtering, and the mining of free text in electronic health records from veterinary practices for the purpose of sentinel surveillance. However, ML is also being applied to tasks that previously relied on traditional statistical data analysis. Statistical models have been used extensively to infer relationships between predictors and disease to inform risk-based surveillance, and increasingly, ML algorithms are being used for prediction and forecasting of animal diseases in support of more targeted and efficient surveillance. While ML and inferential statistics can accomplish similar tasks, they have different strengths, making one or the other more or less appropriate in a given context.

L'apprentissage automatique (AA) est une approche de l'intelligence artificielle caractérisée par l'utilisation d'algorithmes qui améliorent leurs propres performances sur une tâche donnée (par exemple, la classification ou la prédiction) sur la base de données et sans avoir reçu d'instructions spécifiques ou complètes concernant la marche à suivre. Les systèmes de surveillance des maladies animales et des zoonoses sont tributaires de la mise en oeuvre efficace d'un large éventail de tâches, parmi lesquelles certaines sont susceptibles de fonctionner avec des algorithmes d'AA. Comme dans d'autres domaines, l'utilisation de l'AA s'est beaucoup développée ces dernières années dans le secteur de la surveillance de la santé animale et de la santé publique vétérinaire. Les algorithmes d'AA sont utilisés pour accomplir des tâches qui ne sont devenues possibles que grâce à l'arrivée de grandes séries de données, de nouvelles méthodes d'analyse et de capacités informatiques accrues. Parmi les exemples, on peut citer la capacité à déceler une structure sous-jacente dans de grands volumes de données provenant d'un flux continu de registres de saisies d'abattoirs, l'utilisation de l'apprentissage profond pour identifier les lésions révélées par les images numériques obtenues pendant l'abattage et l'extraction de texte libre à partir des registres sanitaires électroniques des cabinets vétérinaires à des fins de surveillance sentinelle. L'AA est cependant également appliqué dans des tâches qui s'appuyaient précédemment sur une analyse classique de données statistiques. Les modèles statistiques ont été largement utilisés pour déduire des relations entre prédicteurs et maladie afin d'étayer la surveillance fondée sur le risque ; les algorithmes d'AA sont de plus en plus utilisés pour prédire et pronostiquer des maladies animales en vue d'une surveillance plus ciblée et efficace. S'il est vrai que l'AA et la statistique inférentielle peuvent accomplir des tâches similaires, chaque approche présente ses propres atouts et pourra se révéler plus ou moins pertinente selon le contexte spécifique.

El aprendizaje automático es una vertiente de la inteligencia artificial que se caracteriza por el uso de algoritmos capaces de mejorarse a sí mismos en la ejecución de una determinada tarea (p.ej., procesos de clasificación o predicción) con empleo de datos y sin necesidad de recibir instrucciones explícitas y completas sobre la manera de lograrlo. Los sistemas de vigilancia de enfermedades animales y zoonóticas dependen de la ejecución eficaz de numerosas y muy diversas tareas, algunas de las cuales se prestan al uso de algoritmos de aprendizaje automático. Al igual que en otros campos, la aplicación del aprendizaje automático en sanidad animal y salud pública veterinaria se ha extendido sobremanera en los últimos años. Ahora se utilizan algoritmos de aprendizaje automático para realizar tareas que solo han empezado a ser factibles con el advenimiento de ingentes conjuntos de datos, nuevos métodos para analizarlos y una mayor capacidad de tratamiento informático. Entre otros ejemplos, cabe citar la determinación de la estructura subyacente de grandes volúmenes de datos procedentes de un flujo continuo de registros de los descartes de matadero; la utilización del aprendizaje profundo para detectar lesiones en imágenes digitales obtenidas durante las operaciones de sacrificio, o el análisis del texto libre de registros sanitarios electrónicos de procedimientos veterinarios con fines de vigilancia centinela. Con todo, el aprendizaje automático se está aplicando también a tareas que anteriormente reposaban en el análisis estadístico clásico de los datos. Los modelos estadísticos han sido extensamente utilizados para inferir relaciones entre una enfermedad y uno u otro predictor y alimentar a partir de ahí la vigilancia basada en el riesgo. Por otro lado, cada vez más se vienen empleando algoritmos de aprendizaje automático para predecir y anticipar enfermedades animales y conferir así más eficacia y especificidad a las actividades de vigilancia. Aunque el aprendizaje automático y la estadística inferencial realizan tareas parecidas, sus puntos fuertes son distintos, con lo cual, en función del contexto de que se trate, será preferible recurrir a uno u otro método.

Economic evaluation of antimicrobial usage surveillance in livestock.

There is increased pressure by governments and industry to develop national surveillance programmes to evaluate antimicrobial usage (AMU) in animals. This article presents a methodological approach to cost-effectiveness analysis of such programmes. Seven objectives are proposed for AMU surveillance in animals: quantifying use, finding trends, detecting hotspots, identifying risk factors, encouraging research, evaluating the impact of policies and diseases, and demonstrating compliance with regulations. Achieving these objectives would assist in making decisions about potential interventions, help to generate trust, incentivise the reduction of AMU and decrease the risk of antimicrobial resistance. The cost-effectiveness of each objective can be found by dividing the cost of the programme by the performance indicators of the surveillance required to meet the objective concerned. The precision and accuracy of surveillance outputs are suggested here as useful performance indicators. Precision depends on the level of surveillance coverage (SC) and surveillance representativeness (SR). Accuracy is influenced by the quality of farm records and SR. The authors argue that there is an increase in marginal cost for each unit increase of SC, SR and data quality. This is caused by the increasing difficulty of recruiting farmers due to potential barriers such as staff capacity, capital availability, computing literacy and availability, and geographical differences, among other factors. A simulation model was conducted to test the approach, using the quantification of AMU as the primary objective, and to provide evidence of the application of the law of diminishing returns. Cost-effectiveness analysis can be used to support decisions on the level of coverage, representativeness and data quality required in such AMU programmes.

Les gouvernements tout comme le secteur de l'élevage exercent une pression croissante pour que des programmes nationaux de surveillance soient élaborés afin d'évaluer l'utilisation d'agents antimicrobiens (UAM) chez les animaux. Cet article présente une approche méthodologique permettant de réaliser l'analyse coût-efficacité de ces programmes. Sept objectifs sont proposés pour la surveillance de l'UAM chez les animaux : quantifier cette utilisation, relever les tendances, détecter les situations d'utilisation intensive, déceler les facteurs de risque, encourager la recherche, évaluer l'impact des politiques et des maladies et démontrer la conformité avec les réglementations. La réalisation de ces objectifs de surveillance permettra de prendre des décisions éclairées sur les interventions à mener, contribuera à mettre en place un climat de confiance, encouragera à réduire l'UAM et atténuera le risque d'apparition d'antibiorésistances. Le ratio coût-efficacité de chaque objectif peut être déterminé en divisant le coût du programme par les indicateurs de performance de la surveillance requise pour chacun des objectifs examinés. Les auteurs considèrent que la précision et l'exactitude des résultats de la surveillance sont des indicateurs de performance utiles à cet effet. La précision dépend du niveau de couverture de la surveillance (CS) et de sa représentativité (RS). L'exactitude est fonction de la qualité des registres d'élevage et de la RS. D'après les auteurs, chaque accroissement unitaire de la CS, de la RS et de la qualité des données donne lieu à une augmentation du coût marginal. Celle-ci s'explique par la difficulté croissante de recruter des éleveurs pour cette activité, en raison d'obstacles tels que le manque d'effectifs, la disponibilité de capitaux, le manque de compétences et d'équipements informatiques et les différences géographiques, entre autres facteurs potentiels. Un modèle de simulation a été mis en oeuvre pour tester cette approche à partir de l'objectif principal (la quantification de l'UAM), et pour apporter des éléments démontrant l'application de la loi des rendements décroissants dans ce domaine. L'analyse coût-efficacité peut être utilisée pour étayer les décisions concernant la couverture, la représentativité et la qualité des données requises pour les programmes de surveillance de l'UAM.

Los gobiernos y la industria vienen presionando cada vez más para la implantación de programas nacionales de vigilancia destinados a evaluar el uso de agentes antimicrobianos (UAM) en los animales. Los autores presentan una solución metodológica para analizar la relación costo-eficacia de tales programas. En primer lugar proponen un conjunto de siete objetivos que deben cumplirse al vigilar el UAM en los animales: cuantificar el uso, detectar tendencias, localizar áreas de "gran intensidad" de uso, determinar los factores de riesgo, alentar la investigación, evaluar la repercusión de las políticas y las enfermedades y comprobar la observancia de los reglamentos. El logro de estos objetivos ayudaría a decidir sobre posibles intervenciones y a generar confianza, supondría un incentivo para reducir el UAM y atenuaría el riesgo de que surgieran resistencias a estos productos. Para cada objetivo es posible determinar la relación costo-eficacia dividiendo el costo del programa por los indicadores de desempeño de la vigilancia requerida para cumplir el objetivo en cuestión. Los autores proponen utilizar la precisión y exactitud de los resultados de la vigilancia como útiles indicadores de desempeño. La precisión depende del nivel de cobertura y de representatividad de la vigilancia. En la exactitud, por su parte, influyen la calidad de los archivos de las explotaciones pecuarias y la representatividad de la vigilancia. Los autores postulan que cada aumento unitario de la cobertura y la representatividad de la vigilancia y de la calidad de los datos se acompaña de un aumento correspondiente del costo marginal. Ello se explica por la creciente dificultad que presenta la participación de ganaderos en el proceso, debida a su vez a posibles barreras en aspectos como la dotación de personal, el capital disponible, los conocimientos en informática y el acceso a ordenadores o las diferencias geográficas, entre otros factores. Para ensayar el método y probar que se aplica el principio de los rendimientos decrecientes, los autores emplearon un modelo de simulación, utilizando como principal objetivo la cuantificación del UAM. El análisis de la relación costo-eficacia puede ser utilizado como herramienta auxiliar para tomar decisiones sobre el nivel de cobertura, representatividad y calidad de los datos que se necesita en este tipo de programas de vigilancia del UAM.

[Cytopathological characterization of ascites for the diagnosis of serous ovarian carcinoma].

Objective: To investigate the cytomorphological and immunocytochemical features of tumor cells in the ascites of ovarian plasmacytoma (SOC). Methods: Specimens of serous cavity effusions were collected from 61 tumor patients admitted to the Affiliated Wuxi People's Hospital of Nanjing Medical University from January 2015 to July 2021, including ascites from 32 SOC, 10 gastrointestinal adenocarcinomas, 5 pancreatic ductal adenocarcinomas, 6 lung adenocarcinomas, 4 benign mesothelial hyperplasia and 1 malignant mesothelioma patients, pleural effusions from 2 malignant mesothelioma patients and pericardial effusion from 1 malignant mesothelioma. Serous cavity effusion samples of all patients were collected, conventional smears were made through centrifugation, and cell paraffin blocks were made through centrifugation of remaining effusion samples. Conventional HE staining and immunocytochemical staining were applied to observe and summarize cytomorphological characteristics and immunocytochemical characteristics. The levels of serum tumor markers carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were detected. Results: Of the 32 SOC patients, 5 had low-grade serous ovarian carcinoma (LGSOC) and 27 had high-grade serous ovarian carcinoma (HGSOC). 29 (90.6%) SOC patients had elevated serum CA125, but the difference was not statistically significant between them and patients with non-ovarian primary lesions included in the study (P>0.05); The serum CEA was positive in 9 patients with gastrointestinal adenocarcinoma and 5 patients with lung adenocarcinoma, and the positive rate was higher than that in SOC patients (P<0.001); The serum CA19-9 was positive in 5 patients with gastrointestinal adenocarcinoma and 5 patients with pancreatic ductal adenocarcinoma, and the positive rate was higher than that in SOC patients (P<0.05). The serum CA125, CEA and CA19-9 were within the normal range in 4 patients with benign mesothelial hyperplasia. LGSOC tumor cells were less heterogeneous and aggregated into small clusters or papillary pattern, and psammoma bodies could be observed in some LGSOC cases. The background cells were fewer and lymphocytes were predominant; the papillary structure was more obvious after making cell wax blocks. HGSOC tumor cells were highly heterogeneous, with significantly enlarged nuclei and varying sizes, which could be more than 3-fold different, and nucleoli and nuclear schizophrenia could be observed in some cases; tumor cells were mostly clustered into nested clusters, papillae and prune shapes; there were more background cells, mainly histiocytes. Immunocytochemical staining showed that AE1/AE3, CK7, PAX-8, CA125, and WT1 were diffusely positively expressed in 32 SOC cases. P53 was focally positive in all 5 LGSOCs, diffusely positive in 23 HGSOCs, and negative in the other 4 HGSOCs. Most of adenocarcinomas of the gastrointestinal tract and lung had a history of surgery, and tumor cells of pancreatic ductal adenocarcinoma tend to form small cell nests. Immunocytochemistry can assist in the differential diagnosis of mesothelial-derived lesions with characteristic "open window" phenomenon. Conclusion: Combining the clinical manifestations of the patient, the morphological characteristics of the cells in the smear and cell block of the ascites can provide important clues for the diagnosis of SOC, and the immunocytochemical tests can further improve the accuracy of the diagnosis.

[Advances in clinical diagnosis and treatment of drug-induced liver injury in children].

Drug-induced liver injury (DILI) is one of the most common adverse drug reactions that may seriously threaten the health of children and is receiving increasing clinical attention day by day. There is still no independent diagnosis and treatment guideline for DILI in children, but its clinical features are not completely similar to those in adults. This article reviews the epidemiology, clinical features, diagnosis, and treatment progress in order to provide a reference for the management of DILI in children.