Modeling of fruit growth of tangor murcott and incidence of environmental factors on the estimated parameters / Modelación de crecimiento de frutos de tangor murcott e incidencia de factores medioambientales sobre los parámetros estimados

ABSTRACT Fruit production forecasts are a tool to plan the harvest and improve market strategies. To carry it out, it is essential to have information about the behavior of fruit development over time. The objective of this work was to find the mathematical-statistical model that best describes the growth pattern of tangor murcott fruit (Citrus reticulata x C. sinensis 'Murcott') and analyze how it is affected by environmental conditions. For this, in nine orchards, located in four locations in the province of Corrientes, Argentina, the equatorial diameter of 2,053 fruit from 82 days after full flowering to harvest were periodically registered during five seasons. The nonlinear models were compared: Logistic, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, and their respective re-parameterizations. The magnitudes of nonlinearity measures, coefficient of determination and estimates of residual deviation were considered as the main goodness-of-fit criteria. The selected model-parameterization combination was the fifth parameterization of the Logistic model with random effects on its three parameters. An Analysis of Variance model on the estimates of these parameters for each fruit showed that orchard and season factors were an important source of variability, mainly in those related to the initial size of the fruit and their growth rate. These results will allow the construction of growth tables, which in addition to making yield predictions, can be used to estimate fruit size distribution at harvest and improve the cultural practice of manual fruit thinning.

RESUMEN Los pronósticos de producción de fruta son una herramienta para planificar la cosecha y mejorar estrategias de mercado. Para su realización es imprescindible contar con información acerca del desarrollo de los frutos a lo largo del tiempo. El objetivo del presente trabajo fue encontrar el modelo matemático-estadístico que mejor describa el patrón de crecimiento de frutos tangor murcott (Citrus reticulata x C. sinensis 'Murcott') y analizar cómo es afectado por condiciones medioambientales. En nueve huertos, ubicados en cuatro localidades en la provincia de Corrientes, Argentina, se registró durante cinco temporadas el diámetro ecuatorial de 2053 frutos desde los 82 días después de plena floración hasta el momento de cosecha. Se compararon los modelos no lineales: Logístico, Gompertz, Brody, Von Bertalanffy, Weibull, Morgan Mercer Flodin (MMF), Richards, y sus respectivas re-parameterizaciones. Como principales criterios de bondad de ajuste se consideraron las magnitudes de medidas de no linealidad, coeficiente de determinación y estimaciones del desvío residual. La combinación modelo-parametrización seleccionada fue la quinta parametrización del modelo Logístico con efectos aleatorios en sus tres parámetros. Un modelo de análisis de la variancia sobre las estimaciones de estos parámetros para cada fruto mostró que los factores huerto y temporada eran una importante fuente de variabilidad, principalmente en los relacionados con el tamaño inicial de los frutos y su tasa de crecimiento. Estos resultados permitirán construir tablas de crecimiento, que además de realizar predicciones de rendimientos, podrán ser utilizadas para estimar distribución de tamaños de fruto a cosecha y mejorar la práctica cultural de raleo.

Diseño de una escala predictiva de mortalidad en pacientes con enfermedad renal crónica

Introducción: La predicción de mortalidad en pacientes con enfermedad renal crónica, mediante escalas o índices pronósticos presenta limitaciones reales. Objetivo: Diseñar una escala predictiva de mortalidad en pacientes con enfermedad renal crónica. Métodos: Se realizó un estudio observacional, analítico, longitudinal prospectivo en 169 pacientes con enfermedad renal crónica desde el 1 de enero de 2022 al 31 de diciembre de 2022. La investigación se desarrolló en 2 etapas: durante los primeros 6 meses del año se analizaron las variables de estudio para el diseño de la escala predictiva. En los próximos 6 meses, los pacientes fueron seguidos para identificar la ocurrencia o no de la variable dependiente mortalidad. Se determinó la capacidad discriminatoria de la escala predictiva y se evaluaron curvas de supervivencia. Resultados: Las variables que conformaron la escala predictiva fueron edad > 65 años, enfermedad cardiovascular, albúmina 390 mmol/L. El poder discriminatorio para predecir mortalidad fue bueno, índice C: 0,856 (IC 95 %: 0,783-0,929; p< 0,001). Los pacientes con valores menores a 4 puntos presentaron media de supervivencia de 149,438 ± 7,296 días. En cambio, los que tenían valores superiores presentaron media de supervivencia de 93,128 ± 8,545 días. Conclusiones: La escala predictiva contribuyó a la estratificación del riesgo de mortalidad de los pacientes. Las variables incluidas son de fácil determinación e interpretación por lo que es un modelo útil en la toma de decisiones médicas en el ámbito clínico actual.

Introduction: The prediction of mortality in patients with chronic kidney disease using scales or prognostic indices has real limitations. Objective: Design a mortality predictive scale in patients with chronic kidney disease. Methods: A prospective observational, analytical, longitudinal study was carried out in 169 patients with chronic kidney disease from January 1, 2022 to December 31, 2022. The research was developed in 2 stages: during the first 6 months of the year, the variables were analyzed for the design of the predictive scale. In the next 6 months, patients were followed to identify the occurrence or not of the dependent variable mortality. The discriminatory capacity of the predictive scale was determined and survival curves were evaluated. Results: The variables that made up the predictive tool were age > 65 years, cardiovascular disease, albumin 390 mmol/L. The discriminatory power to predict mortality was good, C index: 0.856 (95% CI: 0.783-0.929; p< 0.001). Patients with values less than 4 points had a mean survival of 149.438 ± 7.296 days. In contrast, those with higher values presented a mean survival of 93.128 ± 8.545 days. Conclusions: The scale contributed to the stratification of the mortality risk of the patients. The variables included are easy to determine and interpret, making it a useful model for medical decision making in the current clinical setting.

Clinical value of serum vitamin A and basic fibroblast growth factor levels in predicting retinopathy of prematurity / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To investigate the clinical value of serum vitamin A(Vit A)and basic fibroblast growth factor(bFGF)levels predicting retinopathy of prematurity(ROP).METHODS: Prospective cohort studies. A total of 411 premature or low birth weight infants with gestational age less than 37 wk or birth weight less than 2 500 g who were delivered in Hainan Branch, Shanghai Children's Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine from January 2020 to December 2022 were selected as subjects. The Vit A and bFGF levels in peripheral blood were detected at 7 d and 35 d after birth, respectively.RESULTS: A total of 392 premature infants or low birth weight infants completed clinical study, including 51 cases in stage 1-2 ROP group, 23 cases in stage 3-5 ROP group and 318 cases in the group without ROP. At 7 d postnatal, the serum Vit A(0.44±0.17 μmol/L)and bFGF(0.53±0.16 ng/L)levels in stage 1-2 ROP group were lower than those in the group without ROP(0.50±0.12 μmol/L and 0.63±0.15 ng/L; all P&#x0026;#x003C;0.05). The serum Vit A(0.34±0.18 μmol/L)and bFGF(0.44±0.18 ng/L)levels in stage 3-5 ROP group were lower than those in the group without ROP(P&#x0026;#x003C;0.05). The serum Vit A and bFGF levels in stage 3-5 ROP group were lower than those in stage 1-2 ROP group(P&#x0026;#x003C;0.05). At 35d postnatal, the serum Vit A(0.33±0.19 μmol/L)and bFGF(0.39±0.19 ng/L)levels in stage 3-5 ROP group were lower than those in stage 1-2 ROP group(0.43±0.16 μmol/L and 0.48±0.17 ng/L; all P&#x0026;#x003C;0.05). According to the ROC curve drawn by serum Vit A, the AUC value was 0.853, the maximum Youden index was 0.68, the best sensitivity was 73%, and the best specificity was 95%. According to the ROC curve drawn by serum bFGF, the AUC value was 0.828, the maximum Youden index was 0.58, the best sensitivity was 90%, and the best specificity was 68%. According to the ROC curve drawn by serum Vit A combined with bFGF, the AUC value was 0.917, the maximum Youden index was 0.70, the best sensitivity was 70%, and the best specificity was 100%.CONCLUSION: Serum Vit A and bFGF levels are sensitive and effective indicators for predicting ROP. If the serum Vit A or bFGF levels are lower in premature infants or low birth weight infants, it may indicate the higher probability of ROP and its pathological stages. In addition, the clinica value of serum Vit A combined with bFGF in the diagnosis of ROP is higher than that of Vit A or bFGF alone, and the misdiagnosis rate is reduced.

Influence of preoperative HbA1c level on posterior membrane opacity after phacoemulsification in diabetic patients and analysis of threshold effect / 国际眼科杂志(Guoji Yanke Zazhi)

AIM:To analyze the predictive value and threshold effect of preoperative glycated hemoglobin(HbA1c)level on posterior capsular opacification(PCO)in diabetic cataract patients.METHODS:Retrospective study. A total of 106 diabetic patients(106 eyes)with cataract treated in our hospital from September 2018 to September 2020 were collected. The patients were divided into PCO group(52 cases, 52 eyes)and non-PCO group(54 cases, 54 eyes)according to whether PCO occurred at 36 mo after surgery. The risk factors affecting postoperative PCO were analyzed. The threshold effect of HbA1c level on the occurrence of postoperative PCO was analyzed. The predictive value of preoperative HbA1c level in postoperative PCO was evaluated using the receiver operating characteristic(ROC)curve. The prediction model was constructed, and Bootstrap resampling was used to verify the prediction model, and the differentiation and accuracy of the model were evaluated.RESULTS: There were significant differences in diabetes course, diabetic retinopathy, fasting blood glucose, 2 h postprandial blood glucose, preoperative HbA1c, axial length and nuclear grade between PCO and non-PCO groups(P&#x003C;0.05). Duration of diabetes ≥12 a, presence of DR, fasting blood glucose ≥8 mmol/L, 2 h postprandial blood glucose ≥12 mmol/L, preoperative HbA1c ≥7%, axial length ≥30 mm, and nuclear grade Ⅲ or above were all influencing factors for postoperative PCO(P&#x003C;0.05). Curve fitting found that the probability of postoperative PCO showed an increasing trend with the increase of HbA1c level. Threshold effect analysis found that the incidence of postoperative PCO increased with the increase of HbA1c level when HbA1c≥7%. Sensitivity analysis showed that E value=2.129. The analysis of the correlation effect between preoperative HbA1c and the degree of PCO after phacoemulsification showed that the adjusted preoperative HbA1c level was an independent factor affecting the degree of PCO in diabetic patients(OR=1.65, 95% CI: 1.42-1.76, P=0.021). PCO outcome in diabetic cataract patients was indicated when the predictive model P=0.6, and the prediction accuracy of the model was 88.51%. Sensitivity and specificity were 86.33% and 86.82%, respectively.CONCLUSION:Duration of diabetes, presence of DR, fasting blood glucose, 2 h postprandial blood glucose, preoperative HbA1c, axial length, and nuclear grade were independent risk factors for postoperative PCO in diabetic patients, and preoperative HbA1c could be used as a sensitive index to evaluate postoperative PCO.

Evaluation of the effect of meteorological risk forecasting service intervention on acute onset and medical expenses of patients with COPD in Pudong New Area of Shanghai / 上海预防医学

ObjectiveTo evaluate the intervention effect of meteorological risk forecasting service on acute onset and medical expenses of chronic obstructive pulmonary disease（COPD） patients， and to provide scientific basis for the establishment of health management model for chronic obstructive pulmonary disease（COPD） patients. MethodsStudy subjects were recruited from chronic obstructive pulmonary patients aged ≥40 in Pudong New Area. Propensity score matching method was used to determine the intervention group and the control group. The control group received regular health education and follow-up management， and the intervention group was provided with meteorological and environmental risk forecasting services through WeChat， mobile phone short message service（SMS）and telephone. Finally， a total of2 589 subjects were included in the analysis， including 1 300 in the intervention group and 1 289 in the control group. General demographic data， past medical history and family history of COPD， COPD related knowledge and practice survey， COPD related symptom assessment， acute onset， health service utilization and medical expenses before and after intervention were collected through questionnaire survey. The differences of acute attack， health service utilization and related medical expenses between the two groups before and after intervention were compared to evaluate the intervention effect. ResultsIn terms of acute attacks， after intervention， the incidence of acute attacks in the intervention group was lower than that before intervention（χ2=52.901， P<0.001）， and the incidence of acute attacks in the groups with different intervention methods was lower than that before intervention （P<0.001）. WeChat had the best effect， decreasing the incidence by 14.4%， followed by mobile phone SMS SMS decreasing by 12.3%. In terms of utilization of health services， the outpatient rate due to acute attack was lower in the intervention group after intervention than that before intervention （χ2=7.129， P=0.008）， and the outpatient rate due to acute attack was lower in the subjects who received the forecast service through mobile phone SMS than that before intervention （χ2=4.675， P<0.001）. In terms of medical expenses， there was no significant difference between control group and intervention group with different intervention methods before intervention （P>0.05）. After intervention， the difference between the control group and the intervention group with different intervention methods was statistically significant （H=11.864， P<0.05）. The results of multiple comparisons showed that compared with the control group， the average annual medical expenses of patients receiving mobile phone SMS and telephone forecasting services after intervention were lower than those of the control group， and the difference was statistically significant （P<0.05）. ConclusionMeteorological risk forecasting service can reduce the acute onset of COPD， reduce the rate of consultation and medical expenses due to acute onset， and provide scientific basis for the basic COPD health management model.

Construction of ultrasound prediction model for postopera-tive recurrence in patients with papillary thyroid carcinoma after complete endoscopic radical surgery / 中国现代普通外科进展

Objective:To establish an ultrasound prediction model of postoperative recurrence in patients with papillary thyroid carcinoma(PTC)after complete endoscopic radical surgery.Meth-ods:264 patients with PTC who underwent complete endoscopic radical surgery for the first time in our hospital from February 2017 to March 2020 were retrospectively collected.They were divided in-to recurrence group and non recurrence group according to whether there was recurrence after surgery.The clinical data,nodule diameter,nodule number,internal echo,microcalcification and other ultrasonic data of the two groups were compared.Single factor,Lasso and Logistic regression mod-els were used to analyze the influencing factors of postoperative recurrence of PTC patients,and an nomogram model was established based on the selected indicators.Results:Compared with the non recurrence group,the patients in the recurrence group had larger nodule diameter,irregular nod-ule edge,aspect ratio＞1,microcalcification and capsule invasion(P＜0.05).Nodular diameter＞10 mm,irregular edge,aspect ratio＞1,microcalcification and capsule invasion were independent risk factors for postoperative recurrence of PTC patients(P＜0.05).The C-index of the constructed nomogram model was 0.756(95％Cl:0.684～0.830),and the AUC of the ROC curve was 0.895(95％Cl:0.866～0.915);The calibration curve results show that the average deviation is 0.027,and the predic-tion probability fits the actual probability well;The clinical decision curve is far away from the extreme curve and has good clinical applicability.Conclusion:The nomogram model based on nodule size,irregular margin,microcalcification,aspect ratio＞1,and capsule invasion has good accuracy in pre-dicting the recurrence of PTC patients after complete endoscopic radical surgery,and has certain clinical significance.

Análisis de los factores pronósticos de mortalidad en los pacientes con enfermedad renal crónica en hemodiálisis / Analysis of Mortality Prognostic Factors in Patients with chronic Kidney Disease on Hemodialysis

Introducción: La enfermedad renal crónica es una de las principales causas de mortalidad en todo el mundo. La estratificación del riesgo a través del análisis de factores pronósticos podría generar un cambio de paradigma. Objetivo: Analizar los factores pronósticos de mortalidad en los pacientes con enfermedad renal crónica en hemodiálisis. Métodos: Se realizó un estudio no experimental, longitudinal de cohorte retrospectivo en los pacientes con enfermedad renal crónica en hemodiálisis en el Hospital General Docente: Dr. Ernesto Guevara de la Serna durante el período del 1 de enero de 2017 al 31 de diciembre de 2021. En general, se analizaron los factores pronósticos de mortalidad mediante el análisis multivariado de regresión logística binaria y se determinó el porcentaje correcto de clasificación del modelo de regresión. Resultados: Se analizaron como variables pronosticas de mortalidad la enfermedad cardiovascular [B = 3,831; p = 0,000; Exp (B) = 46,118], Albúmina 17 mmol/L [B = 1,326; p = 0,027; Exp (B) = 3,767], glucemia < 4 mmol/L [B = 1,600; p = 0,015; Exp (B) = 4,955] y ganancia de peso interdialítica excesiva [B = 2,243; p = 0,001; Exp (B) = 9,420]. El porcentaje global de clasificación del modelo de regresión logística binaria fue de 89,5 por ciento. Conclusiones: Se analizó el modelo predictivo de regresión logística que presentó una buena precisión con los factores de pronósticos asociados a la mortalidad en los pacientes en hemodiálisis(AU)

Introduction: Chronic kidney disease is one of the main causes of mortality worldwide. Risk stratification through the analysis of prognostic factors could generate a paradigm shift. Objective: To analyze the prognostic factors of mortality in patients with chronic kidney disease on hemodialysis. Methods: A non-experimental, longitudinal retrospective cohort study was carried out on patients with chronic kidney disease on hemodialysis at Dr. Ernesto Guevara de la Serna General Teaching Hospital from January 2017 to December 31, 2021. The prognostic factors of mortality were analyzed using multivariate binary logistic regression analysis and the correct percentage of classification of the regression model was determined. Results: Prognostic variables of mortality were analyzed, such as cardiovascular disease [B = 3.831; p = 0.000; Exp (B) = 46.118], albumin 17 mmol/L [B = 1.326; p = 0.027; Exp (B) = 3.767], blood glucose < 4 mmol/L [B = 1.600; p = 0.015; Exp (B) = 4.955] and excessive interdialytic weight gain [B = 2.243; p = 0.001; Exp(B) = 9.420]. The overall classification percentage of the binary logistic regression model was 89.5percent. Conclusions: The logistic regression predictive model was analyzed, which showed good precision with the prognostic factors associated with mortality in hemodialysis patients(AU)

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

Background: In order to manage outbreaks and plan resources, health systems must be capable of accurately projecting COVID-19 case patterns. Health systems can effectively predict future illness patterns by using mathematical and statistical modelling of infectious diseases. Different methods have been used with comparatively good accuracy for various prediction goals in medical sciences. Some illustrations are provided by statistical techniques intended to forecast epidemic cases. In order to increase healthcare systems readiness, this study aimed to identify the most accurate models for COVID-19 with a high global prevalence of positive cases. Methods: Exponential smoothing model and ARIMA were employed on time series datasets to forecast confirmed cases in upcoming months and hence the effectiveness of these predictive models were compared on the basis of performance measures. Results: It was seen that the ARIMA (0,0,2) model is best fitted with smaller values of performance measures (RMSE=4.46 and MAE=2.86) while employed on the recent dataset for short duration. Holt-Winters Exponential smoothing model was found to be more accurate to deal with a longer period of time series based data. Conclusions: The study revealed that working with recent dataset is more accurate to forecast the number of confirmed cases as compared to the data collected for longer period. The early-stage warnings through these predictive models would be beneficial for governments and health professionals to be prepared with the strategies at different levels for public health prevention.

Comparison of exponential smoothing and ARIMA time series models for forecasting COVID-19 cases: a secondary data analysis

Background: In order to manage outbreaks and plan resources, health systems must be capable of accurately projecting COVID-19 case patterns. Health systems can effectively predict future illness patterns by using mathematical and statistical modelling of infectious diseases. Different methods have been used with comparatively good accuracy for various prediction goals in medical sciences. Some illustrations are provided by statistical techniques intended to forecast epidemic cases. In order to increase healthcare systems readiness, this study aimed to identify the most accurate models for COVID-19 with a high global prevalence of positive cases. Methods: Exponential smoothing model and ARIMA were employed on time series datasets to forecast confirmed cases in upcoming months and hence the effectiveness of these predictive models were compared on the basis of performance measures. Results: It was seen that the ARIMA (0,0,2) model is best fitted with smaller values of performance measures (RMSE=4.46 and MAE=2.86) while employed on the recent dataset for short duration. Holt-Winters Exponential smoothing model was found to be more accurate to deal with a longer period of time series based data. Conclusions: The study revealed that working with recent dataset is more accurate to forecast the number of confirmed cases as compared to the data collected for longer period. The early-stage warnings through these predictive models would be beneficial for governments and health professionals to be prepared with the strategies at different levels for public health prevention.

A Study to Analyse Covid-19 Outbreak Using Multiple Linear Regression: A Supervised Machine Learning Approach

Introduction: Globally, COVID-19 have impacted people's quality of life. Machine learning have recently be-come popular for making predictions because of their precision and adaptability in identifying diseases. This study aims to identify significant predictors for daily active cases and to visualise trends in daily active, posi-tive cases, and immunisations. Material and methods: This paper utilized secondary data from Covid-19 health bulletin of Uttarakhand and multiple linear regression as a part of supervised machine learning is performed to analyse dataset. Results: Multiple Linear Regression model is more accurate in terms of greater score of R2 (=0.90)as com-pared to Linear Regression model with R2=0.88. The daily number of positive, cured, deceased cases are signif-icant predictors for daily active cases (p <0.001). Using time series linear regression approach, cumulative number of active cases is forecasted to be 6695 (95% CI: 6259 - 7131) on 93rd day since 18 Sep 2022, if simi-lar trend continues in upcoming 3 weeks in Uttarakhand. Conclusion: Regression models are useful for forecasting COVID-19 instances, which will help governments and health organisations to address this pandemic in future and establish appropriate policies and recom-mendations for regular prevention.

Epidemiological analysis and prediction of varicella in Liaoning, 2006-2021 / 中国热带医学

@#Abstract: Objective　By collecting and sorting the information of varicella cases reported in Liaoning Province from 2006 to 2021, the epidemiological characteristics were analyzed, and the monthly incidence data were predicted, so as to explore the prevention and control strategy of varicella disease in Liaoning Province. Methods　By collecting the characteristic information of varicella cases in Liaoning Province, epidemiological analysis was carried out on the regional, population, and temporal characteristics of varicella incidence. The monthly incidence data of varicella were fitted with Eviews software, seasonal ARIMA model was used for modeling, and models were selected according to SC and AIC. After modeling, the model was used to predict the incidence data in 2022. Results　The incidence rate of varicella in Liaoning Province has increased in recent years. The onset time was "bimodal distribution", with the main peak occurring from November to January of the next year and the secondary peak occurring from May to June. Since 2019, the onset age has shifted backward. From the original 0-<10 age group with the highest incidence rate, it shifted to the 10-<20 age group with the highest incidence rate. From 2006 to 2021, the incidence of varicella mainly concentrated in people aged 0 to <40 years old, and the incidence rate of the population over 40 years old showed a cliff-like decline. The incidence of chickenpox was higher in the central region of Liaoning Province, such as Shenyang, Dalian, Anshan and Panjin, and relatively low in Huludao, Jinzhou, Fuxin and Liaoyang. The distribution of the population was mainly students, followed by kindergartens and scattered children. ARIMA model of monthly incidence data was established by software as ARIMA (1, 0, 1) (1, 1, 1)12. Conclusions　The incidence rate of varicella in Liaoning Province has been rising in recent years. The incidence is obviously seasonal, and the age group of the affected population has moved backward. It is predicted that the incidence will continue to increase in 2022. The prevention and control of varicella should still be the current key work. In order to reduce the population incidence rate, two-dose vaccination strategies should be vigorously promoted the implementation of the, and the inclusion of varicella vaccine in the immunization program should be achieved as soon as possible.

Interaction effect and predictive efficacy of blood glucose and blood calcium on the prognosis of patients with acute severe pancreatitis / 中国临床药理学与治疗学

AIM: To investigate the interactive effects of blood glucose and blood calcium on the prognostic impact of patients with acute severe pancreatitis (SAP) and to analyze their predictive efficacy on prognosis. METHODS: One hundred and seven patients with SAP admitted to our hospital from September 2019 to October 2022 were selected for the study and were divided into poor and good groups according to their prognosis within 28 d. The blood glucose, blood calcium, modified Marshall score, bedside acute pancreatitis severity score (BISAP) were compared between the two groups before treatment, after 3 d of treatment, and after 7 d of treatment, and the correlation between blood glucose, blood calcium and modified Marshall score and BISAP score was analyzed. The blood glucose levels of patients with different blood calcium were compared. Cox regression was used to analyze the factors associated with prognosis. The presence and type of interaction between blood glucose and blood calcium on prognosis were analyzed using the interaction coefficient γ and relative risk (RR) values. The subject operating characteristic curve (ROC) was used to analyze the predictive efficacy of blood glucose and blood calcium on prognosis. RESULTS: The blood glucose, modified Marshall score, and BISAP score of the adverse group after treatment were higher than those of the good group, while the blood calcium was lower than that of the good group (P<0.05). After 3 and 7 days of treatment, blood glucose was positively correlated with improved Marshall score and BISAP score (P<0.05). The blood glucose level in patients with decreased blood calcium was higher than that in patients with normal blood calcium (P<0.05). The decrease of blood calcium had positive interaction with the increase of blood glucose (P<0.05). After 3 and 7 days of treatment, the AUC of blood glucose combined with blood calcium was greater than that predicted by single index (P< 0.05). CONCLUSION: Blood glucose and blood calcium are related to the severity of the disease in SAP patients. There is an interaction between blood glucose and blood calcium in predicting the prognosis of SAP patients. The combined detection of blood glucose and blood calcium has a certain predictive effect on the prognosis of SAP patients.

Grey model forecasting analysis of licensed pharmacist staffing in retail pharmacies in western China / 中国药房

OBJECTIVE To predict the development trends of licensed pharmacist staffing in retail pharmacies within the western China and provide reference for the formulation of policies related to licensed pharmacists. METHODS Based on the data of retail pharmacies and licensed pharmacists in the western China from 2016 to 2022， a grey model was constructed to analyze and predict the number development trends of retail pharmacies and licensed pharmacists in the western China from 2023 to 2026. RESULTS Currently， the 1∶1 staffing requirement for licensed pharmacists and retail pharmacies had been met in Shaanxi， Guangxi and Gansu. Based on current trends， Inner Mongolia， Chongqing， Yunnan， and Qinghai were expected to meet the 1∶1 staffing requirement for licensed pharmacists and retail pharmacies between 2023 and 2026. Sichuan and Xinjiang were also expected to meet this requirement in the future. However， there was still a significant gap in Guizhou， Xizang， and Ningxia towards achieving the above goals. CONCLUSIONS There is still a discrepancy between the deployment of licensed pharmacists and the national requirements in certain western provinces. Local authorities should formulate relevant policies according to local circumstances. Regions that have already met or will soon achieve the staffing requirement for licensed pharmacists should continue to enhance the quantity and quality of their licensed pharmacist workforce. In areas where meet this criterion in the short term is not feasible， it is necessary to strengthen the development of the licensed pharmacist workforce， and control the number of new retail pharmacies.

ARIMA model in predicting of COVID-19 epidemic for the Southern Africa region

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box­Ljung test. The ARIMA (11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population.

Predicting the Major Organism Species in Tobacco in Cao Bang Province, Vietnam Based on the Weather Conditions

In recent years, some organism species are appearing popularly in the tobacco in Cao Bang province, Vietnam, and caused severe damage to the tobacco yield and quality such as budworm (Helicoverpa assulta Guene), aphid (Myzus persicea Sulzer), and powdery mildew (Erysiphe cichoracearum D.C). To manage them effectively, forecasting and controlling insect pests play an important role in tobacco cultivation. The predictive model was built base on the Skybit, Fuzzy, and Degree-days model to forecast and give suitable control methods for major insect pests in tobacco. This model is run on Excel software and calculated by an IF function for the growth of the organism. Result of the model predicted accurately the tobacco budworm, aphids, and powdery mildew damaging tobacco in Cao Bang in April 2022. Based on the results of prediction, we give proper control methods for each insect pests, preventing the quick growth and development of the organism species in the field, reducing the use of pesticides, and increasing the income of the growers. This model has also applied to forecast other pests in the tobacco in Vietnam. To increase the quality of the prediction, the model will continue to be perfected and completed in the coming years based on the practice field.

The predictive value of time series forecasting model in prehospital emergency medical services demand in Guangzhou / 中华急诊医学杂志

Objective:To study the value of autoregressive integrated moving average (ARIMA) and autoregressive (AR) models in predicting the daily number of ambulances in prehospital emergency medical services demand in Guangzhou.Methods:Matlab simulation software was used to analyze the emergency dispatching departure records in Guangzhou from January 1, 2021 to December 31, 2021. A time series for the number of ambulances per day was calculated. After identifying the time series prediction model, ARIMA(1,1,1), AR(4) and AR(7) models were obtained. These models were used to predict the number of ambulances per day. ARIMA(1,1,1) model divided the time series into the training set and test set. Prony method was used for parameter calculation, and the demands of number of ambulances of the next few months were forecasted. AR(4) and AR(7) models used uniformity coefficient to forecast the demands of number of ambulances on that very day.Results:ARIMA(1,1,1), AR(4) and AR(7) can effectively predict the number of ambulances per day. The prediction fitting error of ARIMA (1,1,1) decreased with the extension of prediction time. The mean absolute percentage error (MAPE) of forecast results of daily vehicle output of emergency dispatching within two months was less than 6% and the predicted results were almost within the 95% confidence interval. The residual analysis of the model verified that the model was significantly effective.Conclusions:ARIMA model can make a long-term within two months and effective prediction fitting of the daily vehicle output of emergency dispatching, and AR model can make a short-term and effective prediction of the daily vehicle output of emergency dispatching.

Complicaciones en los pacientes quemados

Introducción: La lesión por quemadura es uno de los eventos más traumáticos y devastadores que puede sufrir un ser humano. Este evento térmico produce profundas alteraciones en los mecanismos sistémicos de defensa del huésped. Las complicaciones en grandes quemados comienzan en la fase inmediata de la inflamación producida tras sufrir la agresión térmica. Las complicaciones en los pacientes quemados se han asociado con un mal pronóstico, con una alta morbilidad y mortalidad. Objetivo: Describir las complicaciones en los pacientes quemados. Métodos: Se realizó un estudio descriptivo, retrospectivo, de corte transversal para describir las complicaciones en los pacientes quemados que ingresaron en la sala de Caumatología del Hospital Universitario Manuel Ascunce Domenech de la ciudad Camagüey en el período comprendido desde enero de 2021 hasta febrero de 2022. Se estudiaron 32 pacientes en quienes se tuvieron en cuenta las variables: índice de gravedad, complicaciones generales, las complicaciones hidroelectrolíticas y acido básicas, así el estado al egreso de los pacientes. Resultados: Hubo un predominio de pacientes con complicaciones en aquellos lesionados con insulto térmico severo, presentes en 24 pacientes para un 75 %. La infección de la lesión por quemaduras, la hiperglucemia, la anemia y los estados de deshidratación fueron las complicaciones que mayormente se presentaron en los pacientes estudiados. La totalidad de los fallecidos fueron del grupo de pacientes que presentaron complicaciones, ocho pacientes para un 25 %. Conclusiones: La infección de las quemaduras es la principal complicación del paciente quemado. La mayoría de los pacientes de esta serie egresaron vivos.

Introduction: Burn injury is one of the most traumatic and devastating events that a human being can suffer. This thermal event produces profound alterations in the host's systemic defense mechanisms. Complications in major burns begin in the immediate phase of inflammation produced after suffering thermal aggression. Complications in the burned patients are associated with a bad prognosis with a high morbility and mortality. Objective: To decribe the complications in burned patient. Methods: A descriptive, retrospective, cross-section study was carried out to determine the complications in the burned patients that were admitted in the service of Caumatology, of the Universitary Hospital Manuel Ascunce Domenech in Camagüey city between January, 2021 and February, 2022. 32 patients were studied and the following variables were evaluated: severity rate, general complications, and acid-basic and hydroelectrolytic complications, as well as the state at discharge of the patients. Results: There was a predominance of patients with complications, in those injured persons with thermic harsh insult, present in 24 patients for 75 %. The infection of the injury for burns, the hyperglycemia, the anemia and the states of dehydration were the complications that largely showed up in the studied patients. All of the dead persons belonged to patient's group that had complications, eight patients showed (25 %). Conclusions: The infection of the burns continues to be the main complication of the burned patient. Most of the patients in this series were discharged alive.

Well beyond the 60s? The impact of a pandemic on the growth of the older population / Muito além dos 60? Impacto de uma pandemia na dinâmica de crescimento da população idosa

Contrary to expectations that the first decades of the 21st century would experience an increase in lived time, the 2020s cast doubts on the future of old age. The Brazilian population is expected to increase until 2030, when it will reach its maximum, with a total of approximately 215 million inhabitants. A trend of population decline was already in progress and had already been documented, but the pandemic accelerated this process. This study describes a set of projections for the older Brazilian population. The projections were elaborated using the main components method, whose advantages are the possibility of separately projecting the behavior of the three demographic variables (fertility, mortality, and migrations) and obtaining results disaggregated by sex and age groups. Birth data for 2018, 2019, and 2020 suggest a 3.51 and 5.28% decrease in total births between 2018 and 2019 and 2019 and 2020, respectively. Preliminary data for 2021, which indicate the continuation of this trend between 2020­2021, show a 2.32% reduction in the number of births. The hypotheses raised for the mortality patterns, if proven to be accurate, suggest a life expectancy of 72.8 years for men and 76.2 years for women at the final period of the projection, resulting in gains of 4.6 and 2.0 years, respectively. Despite these gains, the levels obtained in 2019, pre-pandemic, would be reached by the male population only between 2035 and 2040.

Contrariando expectativas de que as primeiras décadas do século XXI seriam um tempo de expansão do tempo vivido, os anos 2020 apontam dúvidas com relação ao futuro da velhice. A população brasileira deverá crescer até 2030, quando se projeta que atingirá o seu máximo, com um total de aproximadamente 215 milhões de habitantes. Uma tendência de redução populacional já era documentada e estava em curso, mas a pandemia acelerou o seu movimento. Este artigo apresenta um conjunto de projeções para a população brasileira e idosa. Para a sua elaboração, utilizamos o método das componentes, cujas vantagens são: (a) projetar, isoladamente, o comportamento de cada uma das três variáveis demográficas ­ fecundidade, mortalidade e migrações ­ e (b) obter resultados desagregados por sexo e grupos de idade. Os dados de nascimentos para 2018, 2019 e 2020 apontam para uma diminuição deste total de 3,51% entre 2018 e 2019 e de 5,28% entre 2019 e 2020. Os dados preliminares de 2021, que apontam para uma continuação dessa tendência entre 2020 e 2021 demonstram redução de 2,32% no número de nascimentos. As hipóteses feitas para os padrões de mortalidade, se verificadas, apontam para uma expectativa de vida de 72,8 e 76,2 anos no final do período da projeção, o que resultaria em ganhos de 4,6 e 2,0 anos, para homens e mulheres, respectivamente. Apesar desses ganhos, os níveis obtidos em 2019, pré-pandemia, seriam alcançados pelos homens entre 2035 e 2040.

Modelación matemática de los casos autóctonos de Covid-19. Municipio Bayamo. Febrero- Agosto 2021

El municipio Bayamo acumuló, 8162 casos positivos autóctonos de febrero a agosto en el año 2021, es el centro de la epidemia en la provincia de COVID-19 provocada por el SARS -CoV-2 determinado por el test de Proteína C Reactiva, representa el53,2 % del total de los casos en ese periodo en Granma, muy diferente a lo ocurrido en el año 2020 en el cual la provincia acumuló solamente 185personas contagiadas en nueve meses, con una tasa de 22.6 la más baja de Cuba. La provincia Granma acumuló 119 fallecidos en agosto/2021 que representa el 62,9 % de todos los muertos desde que comenzó la pandemia hasta agosto, lo que indica la alta incidencia de la epidemia que hay en estos momentos. Para la modelación matemática y el análisis de los casos positivos autóctonos de todos los ocurridos durante los meses de febrero a agosto en el año 2021 en Bayamo se obtuvieron polinomios de grado tres y cuatro que modelan el comportamiento de la epidemia durante los siete meses analizados, así como el de los fallecidos durante el mes de agosto en Granma con un carácter predictivo mayor al 98 % en todos los modelos.

The Bayamo municipality accumulated 8162 autochthonous positive cases from February to August in 2021, it is the center of the epidemic in the province of COVID-19 caused by SARS-CoV-2 determined by the C-Reactive Protein test, represents the 53.2% of the total cases in that period in Granma, very different from what happened in 2020 in which the province accumulated only 185 infected people in nine months, with a rate of 22.6, the lowest in Cuba. Granma province accumulated 119 deaths in August / 2021, which represents 62.9% of all deaths since the pandemic began until August, which indicates the high incidence of the epidemic that exists at the moment. For the mathematical modeling and analysis of the autochthonous positive cases of all those that occurred during the months of February to August in 2021 in Bayamo, polynomials of degree three and four were obtained that model the behavior of the epidemic during the seven months analyzed. as well as that of the deceased during the month of August in Granma with a predictive character greater than 98% in all models.

O município de Bayamo acumulou 8.162 casos autóctones positivos de fevereiro a agosto de 2021, é o centro da epidemia na província de COVID-19 causada pelo SARS-CoV-2 determinado pelo teste da Proteína C Reativa, representa 53,2% de o total de casos nesse período no Granma, muito diferente do que aconteceu em 2020 em que a província acumulou apenas 185 pessoas infectadas em nove meses, com uma taxa de 22,6, a mais baixa de Cuba. A província do Granma acumulou 119 mortes em agosto / 2021, o que representa 62,9% de todas as mortes desde o início da pandemia até agosto, o que indica a alta incidência da epidemia que existe no momento. Para a modelagem matemática e análise dos casos positivos autóctones de todos os ocorridos durante os meses de fevereiro a agosto de 2021 em Bayamo, foram obtidos polinômios de grau três e quatro que modelam o comportamento da epidemia durante os sete meses analisados. bem como o dos falecidos durante o mês de agosto no Granma com caráter preditivo superior a 98% em todos os modelos.

La fecundidad en la ciudad de Rosario, Argentina: proyecciones y retroproyecciones de la tasa global de fecundidad y las tasas específicas de fecundidad / Fertility in Rosario City, Argentina: Projections and retro-projections of the global fertility rate and specific fertility rates

Resumen El estudio que da lugar al presente artículo surge a partir de los resultados obtenidos en el marco de un convenio de colaboración firmado por la Dirección General de Estadística de la Municipalidad de Rosario y la Escuela de Estadística de la Facultad de Ciencias Económicas y Estadística de la Universidad Nacional de Rosario. Entre sus objetivos, se plantea el de obtener pronósticos probabilísticos de la fecundidad para la Ciudad de Rosario. Para ello, con base en estadísticas vitales, estimaciones y proyecciones de población se construyen escenarios probables, pasados y futuros, tanto para la tasa global de fecundidad como para las tasas específicas de fecundidad. Los resultados de este estudio, basados en la aplicación de modelos probabilísticos de pronóstico, permiten conocer estructuras y tendencias, pasadas y futuras de la fecundidad, de modo que puedan generarse diagnósticos que sean de utilidad para la evaluación y gestión del sistema de salud o bien para el desarrollo de nuevas políticas públicas. Los resultados indican que Rosario tuvo, tiene y seguirá teniendo un cambio en los patrones de fecundidad más rápido y marcado que el promedio nacional. Si bien este hecho es esperable, en un contexto signado por los avances en la salud pública, que permiten acceder a más y mejor atención en salud reproductiva, la metodología aquí empleada se basa únicamente en la extrapolación de las tendencias, por ello la retroproyección debe ser analizada cuidadosamente. Con posterioridad, en la sección metodológica, se presentan los modelos probabilísticos de pronóstico que se emplean para la obtención de resultados.

Abstract The study that gives rise to this article arises from the results obtained in the framework of a collaboration agreement signed by the Statistical Office of Rosario City and the School of Statistics of the Faculty of Economic Sciences and Statistics (National University of Rosario). Among its objectives is to obtain probabilistic fertility forecasts for Rosario City. For this, based on vital statistics, estimates and population projections, probable scenarios, past and future, are constructed, both for the global fertility rate and the specific fertility rates. The results of this study, based on the application of probabilistic prognostic models, allow to know structures and trends, past and future, of fertility, so that diagnoses can be generated that are useful for the evaluation and management of the health system or good for the development of new public policies. The results indicate that Rosario had, has and will continue to have a change in fertility patterns faster and more marked than the national average. Although this fact is to be expected in a context marked by advances in public health (which allow access to more and better reproductive health care), the methodology used here is based solely on the extrapolation of trends, therefore, the backprojection must be carefully analyzed.