Patient delay of patients with pulmonary tuberculosis in Yantai, China, 2013 to 2022: Analysis of a case series.

Patient delay increases the morbidity and mortality due to tuberculosis (TB). This study aimed to assess patient delay among patients with pulmonary tuberculosis in Yantai from 2013 to 2022, and to analyze factors related to patient delay. Data of patients with pulmonary tuberculosis in Yantai City from 2013 to 2022 were obtained from the Tuberculosis Management Information System of the Chinese Disease Prevention and Control System. Statistical analyses were performed using the SPSS.26.0 software. The trend in patient delay rate was tested using the chi-square trend test. Univariate analyses were performed using the chi-square test, and factors with statistically significant differences in the univariate analysis were included in the binary logistic regression analysis to identify the factors affecting patient delay. Patient delay was defined as an interval of more than 14 days between the onset of clinical symptoms and the patient first visit to a healthcare facility. From 2013 to 2022, the median delay time for patients with pulmonary tuberculosis in Yantai was 28 ±â€…52 days and the patient delay rate was 69.5%. There was an overall increasing trend in the rate of patient delay as the number of years increased. Univariate analyses revealed statistically significant differences in patient delay in terms of age, occupation, patient source, domicile, pathogenetic results, and the presence of comorbidities (all P < .05). The results of logistic regression analysis showed that the age was 20 to 39, 40 to 59, and ≥ 60 years (OR = 1.365, 95%CI: 1.156-1.612; OR = 1.978, 95%CI: 1.660-2.356; OR = 1.767, 95%CI: 1.480-2.110), occupation was domestic and un-employed (OR = 1.188, 95%CI: 1.071-1.317), domicile as mobile population (OR = 1.212, 95%CI: 1.099-1.337), and positive pathogenic results (OR = 1.242, 95%CI: 1.015-1.520) were risk factors for patient delay. Patient delays were serious among pulmonary tuberculosis patients in Yantai City, 2013 to 2022, and patient delay was related to factors such as age, occupation, domicile, patient source, and pathogenetic results.

SCD1 sustains brown fat sympathetic innervation and thermogenesis during the long-term cold exposure.

Brown fat adipose tissue (BAT) is a therapeutic potential target to improve obesity, diabetes and cold acclimation in mammals. During the long-term cold exposure, the hyperplastic sympathetic network is crucial for BAT the maintain the highly thermogenic status. It has been proved that the sympathetic nervous drives the thermogenic activity of BAT via the release of norepinephrine. However, it is still unclear that how the thermogenic BAT affects the remodeling of the hyperplastic sympathetic network, especially during the long-term cold exposure. Here, we showed that following long-term cold exposure, SCD1-mediated monounsaturated fatty acid biosynthesis pathway was enriched, and the ratios of monounsaturated/saturated fatty acids were significantly up-regulated in BAT. And SCD1-deficiency in BAT decreased the capacity of cold acclimation, and suppressed long-term cold mediated BAT thermogenic activation. Furthermore, by using thermoneutral exposure and sympathetic nerve excision models, we disclosed that SCD1-deficiency in BAT affected the thermogenic activity, depended on sympathetic nerve. In mechanism, SCD1-deficiency resulted in the unbalanced ratio of palmitic acid (PA)/palmitoleic acid (PO), with obviously higher level of PA and lower level of PO. And PO supplement efficiently reversed the inhibitory role of SCD1-deficiency on BAT thermogenesis and the hyperplastic sympathetic network. Thus, our data provided insight into the role of SCD1-mediated monounsaturated fatty acids metabolism to the interaction between thermogenic activity BAT and hyperplastic sympathetic networks, and illustrated the critical role of monounsaturated fatty acids biosynthetic pathway in cold acclimation during the long-term cold exposure.

Material stock analysis of urban road from nighttime light data based on a bottom-up approach.

In recent years, there has been an increasing focus on the dynamics of material stock, that is, the basis of material flow in the entire ecosystem. With the gradual improvement of the global road network encryption project, the uncontrolled extraction, processing, and transportation of raw materials impose serious resource concerns and environmental pressure. Quantifying material stocks enable governments to formulate scientific policies because socio-economic metabolism, including resource allocation, use, and waste recovery, can be systematically assessed. In this study, OpenStreetMap road network data were used to extract the urban road skeleton, and nighttime light images were divided by watershed to construct regression equations based on geographical location attributes. Resultantly, a generic road material stock estimation model was developed and applied to Kunming. We concluded that (1) the top three stocks are stone chips, macadam, and grit (total weight is 380 million tons), (2) the proportion of asphalt, mineral powder, lime, and fly ash is correspondingly similar, and (3) the unit area stock decreases as the road grade declines; therefore, the branch road has the lowest unit stock.

A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs.

INTRODUCTION: Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor-node-metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment. METHODS: CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot. RESULTS: A prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased. CONCLUSION: The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.

Comparison of Dosimetric Benefits of Three Precise Radiotherapy Techniques in Nasopharyngeal Carcinoma Patients Using a Priority-Classified Plan Optimization Model.

INTRODUCTION: Although intensity-modulated radiotherapy (IMRT), volumetric-modulated arc therapy (VMAT) and tomotherapy (TOMO) are broadly applied for nasopharyngeal carcinoma (NPC), the best technique remains unclear. Therefore, this study was conducted to address this issue. METHODS: The priority-classified plan optimization model was applied to IMRT, VMAT and TOMO plans in forty NPC patients according to the latest international guidelines. And the dosimetric parameters of planning target volumes (PTVs) and organs at risk (OARs) were compared among these three techniques. The Friedman M test in SPSS software was applied to assess significant differences. RESULTS: The median PGTVnx coverage of IMRT was the lowest (93.5%, P < 0.001) for all T categories. VMAT was comparable to TOMO in OARs clarified as priority I and II, and both satisfied the prescribed requirement. IMRT resulted in a relatively high dose for V25 and V30. Interestingly, subgroup analysis showed that the median PTV coverage of the three techniques was no less than 95% in the early T stage. The heterogeneity index (HI) of PGTVnx in VMAT was better than that in IMRT (P = 0.028). Compared to TOMO, VMAT showed a strong ability to protect eyesight and decrease low-dose radiation volumes. In the advanced T stage subgroup, TOMO numerically achieved the highest median PGTVnx coverage volume compared with VMAT and IMRT (93.61%, 91% and 90%, respectively). The best CI and HI of PCTV-1 were observed in TOMO. Furthermore, TOMO was better than VMAT for sparing the brain stem, spinal cord and temporal lobes (all P < 0.05). However, the median V5, V10, V15, V20 and V25 were significantly higher with TOMO than with VMAT (all P < 0.05). CONCLUSION: In the early T stage, VMAT provides a similar dose coverage and protection of OARs to IMRT, and there are no obvious advantages to choosing TOMO for NPC patients in the early T stage. TOMO may be recommended for patients in the advanced T stage due as it provides the largest dose coverage of PGTVnx and the best protection of the brain stem, spinal cord and temporal lobes. Additionally, more randomized clinical trials are needed for further clarification.

Study on the ability of 3D gamma analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS).

OBJECTIVE: To explore the efficacy and sensitivity of 3D gamma analysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size (DCGS). METHODS: 17 patients' plans for cervical cancer were enrolled (Pinnacle TPS, VMAT), and the DCGS was changed from 2.0 mm to 5.0 mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0 mm as the "reference" data set (RDS), the dose distribution calculated by the rest DCGS as the"measurement"data set (MDS), the 3D gamma passing rates and the (N) TCPs of the all structures under different DCGS were obtained, and then analyze the ability of 3D gamma analysis and (N) TCP model in detecting dose changes and what factors affect this ability. RESULTS: The effect of DCGS on planned dose was obvious. When the gamma standard was 1.0 mm, 1.0 and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3D gamma analysis (all p value < 0.05). With the decline of the standard, 3D gamma analysis' ability to detect this difference shows weaker. When the standard was 1.0 mm, 3.0 and 10.0%, the p value of > 0.05 accounted for the majority. With DCGS = 2.0 mm being RDS, ∆gamma-passing-rate presented the same trend with ∆(N) TCPs of all structures except for the femurs only when the 1.0 mm, 1.0 and 10.0% standards were adopted for the 3D gamma analysis. CONCLUSIONS: The 3D gamma analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former's detection ability has a lot to do with the designed standard, and the latter's capability is related to the parameters and calculated accuracy instrinsically.

Screening and identification of the active components from Puerariae Radix by HUVEC/CMC-LC-MS2.

Puerariae Radix (PR) serves as food and medicinal plant for thousands of years with explicit efficacy for heart diseases, while biological target specifically binding-oriented screening of the active components in PR remains a preliminary stage. Cell membrane chromatography (CMC) is newly developed approach where interactions between active components and certain biological targets can be effectively studied, Human umbilical vein endothelial cell (HUVEC) membrane, with its abundant receptors such as ß and AT1, is most eligible for constructing CMC. In this study, an HUVEC/CMC-LC-MS2 system was developed for screening active components in PR, 11 compounds were screened out and four of them were identified. Besides puerarin, the rest identified are daidzin, pueroside D and 3'-hydroxypuerarin. The study provides more reference for CMC applications and PR exploitation.