Climate change is increasingly recognized as an important driver of shifts in the epidemiological patterns of natural focal diseases through its effects on pathogens, hosts, vectors and their ecological interactions. However, current research largely focuses on associations between individual climatic factors, such as temperature and precipitation, and specific diseases, with comparatively limited attention to the complex interaction within the climate–ecology–society system. Furthermore, the performance, applicability, and limitations of existing early warning models have not been sufficiently evaluated from an integrated perspective. This review synthesized the evidence on the mechanisms through which major climatic factors influence disease transmission of natural focal diseases and examined the characteristics, as well as applications and challenges of existing early warning models. On this basis, an integrated research framework was proposed, together with corresponding response strategies for addressing the uncertainties associated with climate change, with the aim of informing future research, prevention, and control of natural focal diseases under a changing climate.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

OBJECTIVETo examine serum levels of sICAM-1 from normal controls and patients with thyroid diseases (simple goitre, Graves' disease or Hashimoto's thyroiditis) with (125)I-sICAM-1 RIA established in our lab.

METHODSUsing (125)I-sICAM-1 RIA, serum sICAM-1 levels of 400 healthy individuals as the normal group and 1020 patients with simple goitre (SG), Graves' disease (GD) or Hashimoto's thyroiditis (HT) were examined for a comporative chinical study.

RESULTSThe serum level of sICAM-1 (x +/- s) in the normal group was 168.43 +/- 36.23 micro g/L. There was no significant difference between the normal and SG groups (P > 0.05), whereas the serum levels of sICAM-1 in autoimmune thyroid diseases (GD or HT) were higher than those in the normal or SG groups (P < 0.05, respectively). After GD patients received one of three medical treatments, their serum sICAM-1 levels decreased (P < 0.05). After GD patients were treated and their thyroid function decreased to normal, their serum sICAM-1 levels were lower than those in relapsed GD patients (P < 0.05).

CONCLUSIONSsICAM-1 RIA can be used to examine autoimmune thyroid diseases. Serum levels of sICAM-1 can be used as a parameter in diagnosing autoimmune thyroid disease and in evaluating the effects of therapy, drug administration or relapse in GD.

Adult ; Aged ; Female ; Goiter ; blood ; diagnosis ; Graves Disease ; blood ; Humans ; Intercellular Adhesion Molecule-1 ; blood ; Male ; Middle Aged ; Radioimmunoassay ; Thyroiditis, Autoimmune ; blood ; diagnosis