Explore the vast range of titles available.

Background Recently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time. Methods Data on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model. Results There were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71–4.76) and 8.16 (95% CI: 2.93–22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40–2.35) and 3.31 (95% CI: 1.97–5.55) after 5 days of general flooding and heavy flooding, respectively. Conclusions Typhoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

This study aimed to evaluate the efficacy of Lactobacillus delbrueckii ssp. bulgaricus (L. bulgaricus) in improving body weight, obesity-related outcomes, and lipid profiles of overweight people. Thirty-six overweight participants were randomly assigned to either a probiotic or a placebo group. A placebo powder or L. bulgaricus powder (containing 1 × 108 colony-forming unit (CFU) of the probiotic) was administered daily for 12 weeks. Body composition was determined, and blood tests were performed before and after the intervention. L. bulgaricus supplementation under the present condition did not affect the body weight, fat percentage, or body mass index (BMI) of the participants, while it resulted in a notable decrease in blood triglyceride (TG) levels, which corresponded to a lowering of the TG proportion in the composition of large VLDL (L–XXL sized fractions) and HDL (M and L fractions) in the probiotic-treated group. These results suggest that L. bulgaricus supplementation under the current conditions may not be helpful for losing weight, but it has the potential to decrease blood TG levels by modulating TG accumulation in or transport by VLDL/HDL in obese patients. L. bulgaricus supplements may have health-promoting properties in preventing TG-related diseases in overweight people.

Diterpenoids jolkinolide A and B, were first isolated from Euphorbia fischeriana . In our previous research, 19-(Benzyloxy)-19-oxojolkinolide B (19-BJB), a derivative of jolkinolides, was synthesized as a novel ent -abietane diterpene diepoxide. In this study, 19-BJB showed strong in vitro activity against bladder cancer cell lines. DNA damage which was observed through the interaction of 19-BJB with nucleotide chains and affected DNA repair resulted in the activation of checkpoint kinase 1 (Chk1) and checkpoint kinase 2 (Chk2) in bladder cancer cell lines. In vivo testing in nude mice also proved that 19-BJB revealed a potential inhibitory effect on tumor growth. Additionally, the 3D-QSAR models of jolkinolides were established. Briefly, we proved that 19-BJB could potentially be used as a drug to inhibit the growth of bladder tumor.

Estrogen receptor α (ERα) antagonist is the most common treatment for ERα-positive breast cancer. However, compensatory signaling contributes to resistance to ERα antagonists. Thus, to explore the potential agents for targeting compensatory signaling, we screened multiple target inhibitors for breast cancer treatment. We attempted to build a structure-based virtual screening model that can find potential compounds and assay the anticancer ability of these drugs by overall cell survival assay. The downstream compensatory phosphorylated signaling was measured by immunoblotting. Hamamelitannin and glucocheirolin were hits for ERα, phosphoinositide 3-kinase (PI3K), and KRAS proto-oncogene, GTPase (KRAS), which were active against estrogen and epidermal growth factor-triggered proliferation. Additionally, we select aminopterin as a hit for ERα, PI3K, KRAS, and SRC proto-oncogene, non-receptor tyrosine kinase (SRC) with inhibitory activities toward AKT serine/threonine kinase 1 (AKT) and mitogen-activated protein kinase kinase (MEK) signaling. Our structure-based virtual screening model selected hamamelitannin, glucocheirolin, aminopterin, and pemetrexed as compounds that may act as potential inhibitors for improving endocrine therapies for breast cancer.

In this study, we analysed the relationship between meteorological factors and the number of patients with coronavirus disease 2019 (COVID-19). The study period was from 12 April 2020 to 13 October 2020, and daily meteorological data and the daily number of patients with COVID-19 in each state of the United States were collected. Based on the number of COVID-19 patients in each state of the United States, we selected four states (California, Florida, New York, Texas) for analysis. One-way analysis of variance ( ANOVA), scatter plot analysis, correlation analysis and distributed lag nonlinear model (DLNM) analysis were used to analyse the relationship between meteorological factors and the number of patients with COVID-19. We found that the significant influencing factors of the number of COVID-19 cases differed among the four states. Specifically, the number of COVID-19 confirmed cases in California and New York was negatively correlated with AWMD (P < 0.01) and positively correlated with AQI, PM2.5 and TAVG (P < 0.01) but not significantly correlated with other factors. Florida was significantly correlated with TAVG (positive) (P < 0.01) but not significantly correlated with other factors. The number of COVID-19 cases in Texas was only significantly negatively associated with AWND (P < 0.01). The influence of temperature and PM2.5 on the spread of COVID-19 is not obvious. This study shows that when the wind speed was 2 m/s, it had a significant positive correlation with COVID-19 cases. The impact of meteorological factors on COVID-19 may be very complicated. It is necessary to further explore the relationship between meteorological factors and COVID-19. By exploring the influence of meteorological factors on COVID-19, we can help people to establish a more accurate early warning system.

Diterpenoids jolkinolide A and B, were first isolated from Euphorbia fischeriana. In our previous research, 19-(Benzyloxy)-19-oxojolkinolide B (19-BJB), a derivative of jolkinolides, was synthesized as a novel ent -abietane diterpene diepoxide. In this study, 19-BJB showed strong in vitro activity against bladder cancer cell lines. DNA damage which was observed through the interaction of 19-BJB with nucleotide chains and affected DNA repair resulted in the activation of checkpoint kinase 1 (Chk1) and checkpoint kinase 2 (Chk2) in bladder cancer cell lines. In vivo testing in nude mice also proved that 19-BJB revealed a potential inhibitory effect on tumor growth. Additionally, the 3D-QSAR models of jolkinolides were established. Briefly, we proved that 19-BJB could potentially be used as a drug to inhibit the growth of bladder tumor.

Diterpenoids jolkinolide A and B, were first isolated from Euphorbia fischeriana. In our previous research, 19-(Benzyloxy)-19-oxojolkinolide B (19-BJB), a derivative of jolkinolides, was synthesized as a novel ent -abietane diterpene diepoxide. In this study, 19-BJB showed strong in vitro activity against bladder cancer cell lines. DNA damage which was observed through the interaction of 19-BJB with nucleotide chains and affected DNA repair resulted in the activation of checkpoint kinase 1 (Chk1) and checkpoint kinase 2 (Chk2) in bladder cancer cell lines. In vivo testing in nude mice also proved that 19-BJB revealed a potential inhibitory effect on tumor growth. Additionally, the 3D-QSAR models of jolkinolides were established. Briefly, we proved that 19-BJB could potentially be used as a drug to inhibit the growth of bladder tumor.

This study aimed to evaluate the efficacy of Lactobacillus delbrueckii ssp. bulgaricus (L. bulgaricus) in improving body weight, obesity-related outcomes, and lipid profiles of overweight people. Thirty-six overweight participants were randomly assigned to either a probiotic or a placebo group. A placebo powder or L. bulgaricus powder (containing 1 × 10[sup.8] colony-forming unit (CFU) of the probiotic) was administered daily for 12 weeks. Body composition was determined, and blood tests were performed before and after the intervention. L. bulgaricus supplementation under the present condition did not affect the body weight, fat percentage, or body mass index (BMI) of the participants, while it resulted in a notable decrease in blood triglyceride (TG) levels, which corresponded to a lowering of the TG proportion in the composition of large VLDL (L–XXL sized fractions) and HDL (M and L fractions) in the probiotic-treated group. These results suggest that L. bulgaricus supplementation under the current conditions may not be helpful for losing weight, but it has the potential to decrease blood TG levels by modulating TG accumulation in or transport by VLDL/HDL in obese patients. L. bulgaricus supplements may have health-promoting properties in preventing TG-related diseases in overweight people.

Burgess Shale–type fossil Lagerstätten provide the best evidence for deciphering the biotic patterns and magnitude of the Cambrian explosion. Here, we report a Lagerstätte from South China, the Qingjiang biota (∼518 million years old), which is dominated by soft-bodied taxa from a distal shelf setting. The Qingjiang biota is distinguished by pristine carbonaceous preservation of labile organic features, a very high proportion of new taxa (∼53%), and preliminary taxonomic diversity that suggests it could rival the Chengjiang and Burgess Shale biotas. Defining aspects of the Qingjiang biota include a high abundance of cnidarians, including both medusoid and polypoid forms; new taxa resembling extant kinorhynchs; and abundant larval or juvenile forms. This distinctive composition holds promise for providing insights into the evolution of Cambrian ecosystems across environmental gradients.

International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F -score, 0.667 (F -score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F -score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.