Trying to manage the dramatic coronavirus disease 2019 (COVID-19) infection spread, many countries imposed national lockdown, radically changing the routinely life of humans worldwide. We hypothesized that both the pandemic per se and the consequent socio-psychological sequelae could constitute stressors for Italian population, potentially affecting the endocrine system. This study was designed to describe the effect of lockdown-related stress on the hypothalamic-pituitary-thyroid (HPT) axis in a cohort of young men. A prospective, observational clinical trial was carried out, including patients attending the male infertility outpatient clinic before and after the national lockdown for COVID-19 pandemic. The study provided a baseline visit performed before and a follow-up visit after the lockdown in 2020. During the follow-up visit, hormonal measurements, lifestyle habits and work management were recorded. Thirty-one male subjects were enrolled (mean age 31.6 ± 6.0 years). TSH significantly decreased after lockdown (p = 0.015), whereas no significant changes were observed in the testosterone, luteinising hormone, follicle-stimulating hormone, estradiol and prolactin serum levels. No patient showed TSH serum levels above or below reference ranges, neither before nor after lockdown. Interestingly, TSH variation after lockdown was dependent on the working habit change during lockdown (p = 0.042). We described for the first time a TSH reduction after a stressful event in a prospective way, evaluating the HPT axis in the same population, before and after the national lockdown. This result reinforces the possible interconnection between psychological consequences of a stressful event and the endocrine regulation.Clostridioides difficile infections (CDIs) are an urgent public health threat worldwide and are a leading cause of morbidity and mortality in healthcare settings. The increasing incidence and severity of infections combined with the scarcity of effective anti-CDI agents has made treatment of CDI very challenging. Therefore, development of new, effective anticlostridial agents remains a high priority. The current study investigated the in vivo efficacy of auranofin in a CDI hamster model. All hamsters treated with auranofin (5 mg/kg) survived a lethal challenge with C. difficile. Furthermore, auranofin (5 mg/kg) was as effective as vancomycin, the drug of choice for treatment of CDIs, against relapsing CDI. Furthermore, auranofin (5 mg/kg) generated a 3.15-log10 reduction (99.97%) in C. difficile count in the cecal contents of hamsters. These results indicate that auranofin warrants further investigation as a new agent to replenish the pipeline of anti-CDI therapeutics.DNA damage activates cell cycle checkpoint proteins ATR and CHK1 to arrest cell cycle progression, providing time for repair and recovery. Consequently, inhibitors of ATR (ATRi) and CHK1 (CHK1i) enhance damage-induced cell death. Intriguingly, both CHK1i and ATRi alone elicit cytotoxicity in some cell lines. Sensitivity has been attributed to endogenous replications stress, but many more cell lines are sensitive to ATRi than CHK1i. Endogenous activation of the DNA damage response also did not correlate with drug sensitivity. Sensitivity correlated with the appearance of γH2AX, a marker of DNA damage, but without phosphorylation of mitotic markers, contradicting suggestions that the damage is due to premature mitosis. Sensitivity to ATRi has been associated with ATM mutations, but dysfunction in ATM signaling did not correlate with sensitivity. CHK1i and ATRi circumvent replication stress by reactivating stalled replicons, a process requiring a low threshold activity of CDK2. In contrast, γH2AX induced by single agent ATRi and CHK1i requires a high threshold activity CDK2. Hence, phosphorylation of different CDK2 substrates is required for cytotoxicity induced by replication stress plus ATRi/CHK1i as compared to their single agent activity. In summary, sensitivity to ATRi and CHK1i as single agents is elicited by premature hyper-activation of CDK2.Although it is well-documented that invasion of invasive plants is promoted with allelopathic effects by inhibiting the growth and phenotypic performance of native plants, little is known conversely. In this study, the allelopathy effects of a native plant, Humulus scandens (Lour.) Merr., on a typical invasive species Alternanthera philoxeroides (Mart.) Griseb., was investigated by exposing A. philoxeroides seedlings to three chemical solvent extracts (i.e., petroleum ether extract (PE), ethyl acetate extract (EE), and n-butanol extract (NE) of H. scandens root (HR). The three chemical extracts inhibited the growth, stem length, node number, leaf number, leaf area, and root number, and increased malondialdehyde (MDA) content of A. philoxeroides seedlings, which indicated that the extracts inhibited the plant growth by damaging the membrane system of leaves. And the synthetical effect of allelopathy (SE) index indicated that EE had the greatest inhibition on the growth of A. philoxeroides. Fifty compounds were identified from the three extracts of HR using GC-MS analysis, among which 5 compounds (dibutyl phthalate, stigmasta-3,5-diene, 2,6-Di-tert-butylphenol campesterol, and neophytadiene) were identified from H. scandens root extracts for the first time. And n-hexadecanoic acid exists in all three extracts. The findings of the present study provide a novel method to potentially control the invasion of A. https://www.selleckchem.com/products/mrtx849.html philoxeroides. However, field monitoring under natural conditions would be necessary to confirm in practice the results obtained with the bioassays.Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format-the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract-transform-load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data.