BACKGROUND: Gastric cancer is a major health problem worldwide, with a high incidence among older adults. Given the aging overall population, it was crucial to understand the current burden and prospective trend of older gastric cancer. This study aimed to analyze the temporal trends of the incidence, mortality, and survival of older gastric cancer in the highest gastric cancer risk area in China from 2010 to 2019, and to predict the future burden of older gastric cancer up to 2024.
METHODS: The study was conducted in Gansu province, an area characterized by the highest gastric cancer incidence and mortality in China. The registration data of gastric cancer incidence and mortality from 2010 to 2019 were pooled from registries in the Gansu Cancer Registration System, while survival data were collected from the First Hospital of Lanzhou University, Lanzhou University Second Hospital, and Gansu Cancer Hospital. Chinese standard population in 2000 and the Segi\'s world standard population were applied to calculate the age-standardized rate. Joinpoint regression was used to analyze the average annual percentage change (AAPC) in cancer incidence and mortality. Autoregressive Integrated Moving Average (ARIMA) models were employed to generate forecasts for incidence and mortality from 2020 to 2024.
RESULTS: Based on registry data from 2010 to 2019, the incidence and mortality rates of gastric cancer among older adults remained stable. The incidence rates declined from 439.65 per 100,000 in 2010 to 330.40 per 100,000 in 2019, with an AAPC of -2.59% (95% confidence interval[CI], -5.14 to 0.04, P = 0.06). Similarly, the mortality rate changed from 366.98 per 100,000 in 2010 to 262.03 per 100,000 in 2019, with an AAPC of -2.55% (95% CI, -8.77-4.08%, P = 0.44). In the hospital-based cohort, the decline in survival rates was reported among older patients with gastric cancer in the highest gastric cancer risk area in China, with the 3-year overall survival (OS) decreasing from 58.5% (95% CI, 53.5-63.2%) in 2010 to 34.4% (95%CI, 32.1-36.7%) in 2019, and the 3-year progression-free survival (PFS) decreasing from 51.3% (95%CI, 47.5-55.1%) in 2010 to 34.2% (95%CI, 32.0-36.3%) in 2019, respectively. Moreover, forecasts generated by ARIMA models revealed a significant decline in the incidence and mortality of older gastric cancer in China from 2020 to 2024. Specifically, the incidence rate of older gastric cancer was expected to decrease from 317.94 per 100,000 population in 2020 to 205.59 per 100,000 population in 2024, while the anticipated mortality rate was estimated to decrease from 222.52 per 100,000 population in 2020 to 186.22 per 100,000 population in 2024.
CONCLUSIONS: From 2010 to 2019, the incidence and mortality of older gastric cancer remained stable in the highest gastric cancer risk area in China, while the survival rates showed a decline. Based on the ARIMA models, it was anticipated that there might be a continued decline in older gastric cancer incidence and mortality in the highest-risk area in China over the next five years.

img src=\"/images/viorel_scripcariu.jpg\" alt=\"Viorel Scripcariu\" style=\"float: right;max-width: 30%;\"/ Assuming the leadership of the Romanian Society of Surgery for the next four years is for me a moment of deep significance and responsibility. With a tradition of almost 130 years, this organization is at the foundation of the Romanian surgeon community, and my role is to continue this legacy and open new paths for the future of Romanian surgery. I would like to share the priorities I have set for this mandate, given our shared commitment to promote excellence in surgical practice and to respond to contemporary challenges. An essential aspect of my mandate is to respect the rich tradition and history of Romanian surgery. The Romanian Society of Surgery was founded at the end of the 19th century in Bucharest, with the surgeon Constantin Severeanu at its head. At the meeting on January 27, 1899, the society constituted its first board, with Thoma Ionescu as president, together with other founding members such as Dr. Leonte, Dr. Racoviceanu-Pitesti, Dr. Duma and Dr. Staicovici. Over the years, numerous meetings were organized at the Institute of Topographic Anatomy in Bucharest, where clinical cases, new surgical methods and lectures by medical personalities from abroad were presented. Over the years, the society has continued to promote international collaboration, organizing congresses and inviting renowned surgeons from abroad to lecture and perform innovative surgery in Romania. This rich history and respect for traditional values are fundamental to preserving and enhancing the standards of excellence we have inherited. We will continue our efforts to honor our mentors and their achievements, ensuring that their legacy will continue to inspire and guide new generations of Romanian surgeons. [ a href=\"https://revistachirurgia.ro/pdfs/2024-4-357.pdf\" read more /a ].

The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a \"desire to have children\" and 49 indicating \"reluctance to have children\") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman\'s desire to have children. Conversely, the number of daughters, the wife\'s ethnicity, and the spouse\'s ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reservoir design and management assistance. This study aims to generalize the machine learning model using the support vector machine (SVM), which is support vector regression (SVR), to predict the discharges of the Euphrates River upstream of the Haditha Dam reservoir in Anbar province West of Iraq. Time series data were collected for the period (1986-2024) for the river\'s daily, monthly, and seasonal flow. Different kernel functions of SVR were applied in this study. The kernels are linear, Quadratic, and Gaussian (RBF). The results showed that the daily time scale is better than the monthly and seasonal performance. In contrast, the linear kernel outperformed the other SVR kernel with a time delay of one day based on the value of the coefficient of determination (R2 = 0.95) and the root mean square error (RMSE = 53.29) m3/sec for predicting daily river flow. The results showed that the proposed machine learning model performed well in predicting the daily flow of the Euphrates River upstream of the Haditha Dam reservoir; this indicates that the model might effectively forecast flows, which helps improve water resource management and dam operations.

While very much in its infancy in terms of becoming an established tool, the use of digital technology in community nursing is steadily growing, despite the persistent barriers to, and challenges encountered in its uptake and implementation. The mobile nature and high workload of a community nurse\'s daily practice should facilitate the rapid uptake of time-saving technology. However, there are indications that technology may not be the panacea it was originally proclaimed to be. Francesca Ramadan elaborates on the past and present applications of digital technology in community nursing and delves into the principles that should shape the future potential of tools such as artificial intelligence, automation technologies and clinical decision support systems.

UNASSIGNED: Cardiovascular disease, type 2 diabetes, and stroke are significant global health concerns. However, gaps persist in understanding the impact of these disorders on women of reproductive age in Central Asia. This study aimed to analyze the health policies implemented in Central Asian countries to address the healthcare needs of this demographic and to forecast future trends in prevalence rates.
UNASSIGNED: We forecasted future trends in prevalence rates, years of life lost, years lived with disability, and disability-adjusted life years for cardiovascular disease, type 2 diabetes, and stroke using publicly available data. Two data sources were utilized: health policy documents issued by the governments of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, and Turkmenistan, and data from the Institute for Health Metrics and Evaluation. Forecasting models, including ARIMA, were employed to predict trends until 2030.
UNASSIGNED: The results indicate an anticipated increase in cardiovascular disease prevalence from 1856.55 in 2020 to 2007.07 by 2029 in Kazakhstan, a subtle increase in Kyrgyzstan from 2492.22 to 2558.69 over 10 years, and similar trends in other countries.
UNASSIGNED: The analysis of policy documents revealed a lack of specific focus on addressing cardiovascular disease, stroke, or type 2 diabetes outside the contexts of pregnancy and childbirth. Understanding these trends is crucial for informing targeted health interventions and resource allocation to mitigate the impact of these diseases on women\'s health in Central Asia.

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A detailed clinical-epidemiological analysis of feline sporotrichosis was conducted, and 288 cases reported between the years 2007 and 2018 were analyzed. The studied cases primarily involved mongrel cats (240/260), males (212/282), and adults (121/200). The main objectives were to identify the risk factors, calculate the monthly incidence rates, and establish a predictive model using the seasonal autoregressive integrated moving average (SARIMA) approach. The statistical analysis revealed significant associations (p < 0.05) between prolonged lesion evolution times and factors such as respiratory signs, prior treatments, and lesion contact. Empirical treatment was identified as a significant risk factor for disease progression. Moreover, the number of cases demonstrated an increasing trend over the study period, with annual peaks noted in disease incidence. The SARIMA model proved to be an effective tool for forecasting the incidence of sporotrichosis, offering robust support for epidemiological surveillance and facilitating targeted public health interventions in endemic regions. The predictive accuracy of the developed model underscored its utility in enhancing disease monitoring and supporting proactive health measures for the effective management of sporotrichosis.

The team is a fundamental and structuring dimension of care, founded on the principles of complementarity, interdependence, and shared objectives and responsibilities towards the patient. As a result of the pandemic and the rationalization of public hospitals, teams are faced with changes in the role of supervisors and the arrival of new figures such as advanced practice nurses. While these changes can bring new dynamism and questioning of practices, they can also be destabilizing. The institution must preserve the role of the team and its members.

Quantifying the impact of competition on individual tree biomass and its distribution pattern can provide a basis for improving the prediction accuracy of forest biomass models. To accurately quantify the effects of competition factors on individual biomass and its distribution, we constructed three different individual biomass models by using nonlinear coupling equations based on the biomass survey data of 50 Larix gmelinii from 18 plots of Pangu Forest Farm in Daxing\'an Mountains. M-1 was a traditional singly additive biomass model. M-2 and M-3 were models taking the distance dependent simple competition index (CI) and distance independent relative diameter (Rd) into account, respectively. Those models were used to reveal the influence of competition factors on the prediction accuracy and distribution pattern of single tree biomass model of L. gmelinii. The results showed that the adjusted R2 of three additive models ranged from 0.694 to 0.974, mean prediction errors ranged from -0.017 to 0.021, and mean absolute errors ranged from 0.152 to 0.357. The introduction of Rd could improve the fitting degree and prediction accuracy of most biomass models, but CI did not affect the model fitting effect and prediction ability. Among the three models, M-3 model had the best performance, with good fitting degree and prediction accuracy of the biomass of each part, which could accurately estimate the single tree biomass of L. gmelinii. Further simulation results showed that the variation of biomass with DBH was mainly affected by CI and Rd grade, and the influence of Rd was stronger than CI. CI had greater influence on root and dry biomass, but less influence on branch and leaf biomass. Rd had a more significant effect on biomass of branch and leaf than on that of root and trunk.
量化竞争对单木生物量及其分配格局的影响,可以为提高林木生物量模型预估精度提供基础。本研究以大兴安岭地区盘古林场18块固定样地中50株兴安落叶松的生物量调查数据为基础,采用非线性联立方程组构建含不同竞争因子(与距离有关的简单竞争指标CI和与距离无关的林木相对直径Rd)的对数尺度单木聚合型二元可加性生物量模型M-2和M-3,并与传统一元可加性生物量模型M-1进行比较,量化竞争因子对兴安落叶松天然林单木生物量模型预估精度及其分配格局的影响。结果表明: 3种可加性模型的调整后确定系数为0.694～0.974,平均预测误差为-0.017～0.021,平均绝对误差为0.152～0.357。引入Rd可以提高绝大多数生物量模型的拟合效果和预测能力,而引入CI对绝大多数生物量模型拟合效果和预测能力的影响不显著。3种模型中,M-3对各部分生物量具有较好的拟合效果和预测能力,可以对兴安落叶松单木生物量进行较好的估计。模拟结果显示,生物量随胸径的变化受CI、Rd等级的影响,其中Rd较CI影响更大;CI对树根和树干生物量影响较大,对树枝、树叶生物量影响较小;Rd对树枝、树叶生物量的影响较树根、树干生物量更大。.