A clinical study of the effects of lead poisoning on the intelligence and neurobehavioral abilities of children

BACKGROUND: Lead is a heavy metal and important environmental toxicant and nerve poison that can destruction many functions of the nervous system. Lead poisoning is a medical condition caused by increased levels of lead in the body. Lead interferes with a variety of body processes and is toxic to many organs and issues, including the central nervous system. It interferes with the development of the nervous system, and is therefore particularly toxic to children, causing potentially permanent neural and cognitive impairments. In this study, we investigated the relationship between lead poisoning and the intellectual and neurobehavioral capabilities of children. METHODS: The background characteristics of the research subjects were collected by questionnaire survey. Blood lead levels were detected by differential potentiometric stripping analysis (DPSA). Intelligence was assessed using the Gesell Developmental Scale. The Achenbach Child Behavior Checklist (CBCL) was used to evaluate each child’s behavior. RESULTS: Blood lead levels were significantly negatively correlated with the developmental quotients of adaptive behavior, gross motor performance, fine motor performance, language development, and individual social behavior (P < 0.01). Compared with healthy children, more children with lead poisoning had abnormal behaviors, especially social withdrawal, depression, and atypical body movements, aggressions and destruction. CONCLUSION: Lead poisoning has adverse effects on the behavior and mental development of 2–4-year-old children, prescribing positive and effective precautionary measures

First detection of Cryptosporidium spp. in red-bellied tree squirrels (Callosciurus erythraeus) in China

Cryptosporidium spp. are opportunistic pathogens that cause diarrhea in a variety of animal hosts. Although they have been reported in many animals, no information has been published on the occurrence of Cryptosporidium spp. in red-bellied tree squirrels (Callosciurus erythraeus). A total of 287 fecal specimens were collected from Sichuan province in China; the prevalence of Cryptosporidium spp., measured by nested-PCR amplification of the partial small-subunit (SSU) rRNA gene, was 1.4% (4/287). Three different Cryptosporidium species or genotypes were identified: Cryptosporidium parvum (n = 1), Cryptosporidium wrairi (n = 1), and Cryptosporidium rat genotype II (n = 2). The present study is the first report of Cryptosporidium infection in red-bellied tree squirrels in China. Although there is a relatively low occurrence of Cryptosporidium, the presence of C. parvum and C. wrairi, which were previously reported in humans, indicates that red-bellied tree squirrels may be a source of zoonotic cryptosporidiosis in China

TELEX HEBDOMADAIRE NR 95 DU 17.09.82 DESTINE A L'ENSEMBLE DES DELEGATIONS EXTERIEURES ET BUREAUX DE PRESS ET D'INFORMATION INDEPENDANTS DANS LES PAYS TIERS = WEEKLY MEMO NO. 95 FOR 17.09.82 TO FOREIGN DELEGATIONS AND PRESS BUREAUS OF THIRD COUNTRIES

<p>High-performance liquid chromatography (HPLC) results of (A) commercial surfactin sample, and (B) our extract surfactin of <i>B</i>. <i>subtilis</i> HH2 in LB medium. There were three main peaks (Peak A-C) of the extract and the surfactin standard in the same location.</p

A COVID-19 Risk Score Combining Chest CT Radiomics and Clinical Characteristics to Differentiate COVID-19 Pneumonia From Other Viral Pneumonias

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

IAPV-Induced Paralytic Symptoms Associated with Tachypnea via Impaired Tracheal System Function

Although it had been reported that Israeli acute paralysis virus (IAPV) can cause systemic infection in honey bees, little is known about how it establishes this infection and results in the typical symptoms, paralysis and trembling. Here, we used our previously constructed IAPV infectious clone to investigate viral loads in different tissues of honey bees and further identify the relation between tissue tropism and paralytic symptoms. Our results showed that tracheae showed a greater concentration of viral abundance than other tissues. The abundance of viral protein in the tracheae was positively associated with viral titers, and was further confirmed by immunological and ultrastructural evidence. Furthermore, higher viral loads in tracheae induced remarkable down-regulation of succinate dehydrogenase and cytochrome c oxidase genes, and progressed to causing respiratory failure of honey bees, resulting in the appearance of typical symptoms, paralysis and body trembling. Our results showed that paralysis symptoms or trembling was actually to mitigate tachypnea induced by IAPV infection due to the impairment of honey bee tracheae, and revealed a direct causal link between paralysis symptoms and tissue tropism. These findings provide new insights into the understanding of the underlying mechanism of paralysis symptoms of honey bees after viral infection and have implications for viral disease prevention and specific therapeutics in practice